The document discusses complex systems and networks, focusing on digital networks. It describes how network models can help understand complex systems like the internet and financial networks. Digital networks have significant power to influence behaviors and spread information. While this power in a single network could be problematic, models show how diversity across multiple competing networks can allow for coexistence, though this is fragile. Sustaining diversity requires balancing viral and mass media influences.
Structure and dynamics of multiplex networks: beyond degree correlationsKolja Kleineberg
The organization of constituent network layers to multiplex networks has recently attracted a lot of attention. Here, we show empirical evidence for the existence of relations between the layers of real multiplex networks that go beyond degree correlations. These relations consist of correlations in hidden metric spaces that underlie the observed topology. We discuss the impact and applications of these relations for trans-layer link prediction, community detection, navigation, game theory, and especially for the robustness of multiplex networks against random failures and targeted attacks. We show that these relations lead to fundamentally new behaviors, which emphasizes the importance to consider organizational principles of multiplex networks beyond degree correlations in future research.
Towards a democratic, scalable, and sustainable digital futureKolja Kleineberg
The document discusses the need for a democratic, scalable, and sustainable digital future. It suggests that digital diversity is possible but fragile. Research shows that routing performance can be improved by using multiple networks simultaneously if they exhibit geometric correlations between node coordinates. Incentives like "Social Bitcoin" could sustain a diverse, decentralized digital world by rewarding users for routing information. The goal is for self-organization of the digital world to create a desirable future with robust digital diversity and efficient search/navigation.
Is bigger always better? How local online social networks can outperform glob...Kolja Kleineberg
The overwhelming success of online social networks, the key actors in the cosmos of the Web
2.0, has reshaped human interactions on a worldwide scale. To help understand the fundamental
mechanisms which determine the fate of online social networks at the system level, we describe the
digital world as a complex ecosystem of interacting networks. In this paper, we discuss the impact
of heterogeneity in network fitnesses induced by competition between an international network,
such as Facebook, and local services.To this end, we construct a 1:1000 scale model of the digital
world, consisting of the 80 countries with the most Internet users. We show how inter-country social
ties induce increased fitness of the international network. Under certain conditions, this leads to
the extinction of local networks; whereas under different conditions, local networks can persist and
even dominate the international network completely. These findings provide new insights into the
possibilities for preserving digital diversity.
Hidden geometric correlations in real multiplex networksKolja Kleineberg
Read the paper at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3812.html
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Collective navigation of complex networks: Participatory greedy routingKolja Kleineberg
Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.
Geometric correlations in multiplexes and how they make them more robustKolja Kleineberg
This document discusses research on the structure and dynamics of multiplex networks. It begins by introducing the concept of multiplex networks, which have the same nodes existing across different network layers. It then discusses how degree correlations and geometric correlations between the positions of nodes in the hidden metric spaces underlying different network layers have been found in real multiplex systems. The document explores how these geometric correlations allow for applications like better identifying communities of nodes, improved link prediction between layers, and more efficient navigation across the network layers.
Ecology 2.0: Coexistence and domination among interacting networksKolja Kleineberg
The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of these services for the first time has allowed researchers to quantify large-scale social patterns. However, the mechanisms that determine the fate of networks at a system level are still poorly understood. For instance, the simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.
The Hidden Geometry of Multiplex Networks @ Next Generation Network Analytics Kolja Kleineberg
The document summarizes research on the hidden geometry of multiplex networks. It finds that real-world multiplex networks often have correlated geometric properties between network layers, with nodes maintaining similar radial and angular coordinates. This has implications like communities of nodes being similar across layers and hyperbolic distance in one layer predicting connections in another. A geometric multiplex model is introduced to generate realistic multiplex networks with tunable geometric correlations between layers.
Structure and dynamics of multiplex networks: beyond degree correlationsKolja Kleineberg
The organization of constituent network layers to multiplex networks has recently attracted a lot of attention. Here, we show empirical evidence for the existence of relations between the layers of real multiplex networks that go beyond degree correlations. These relations consist of correlations in hidden metric spaces that underlie the observed topology. We discuss the impact and applications of these relations for trans-layer link prediction, community detection, navigation, game theory, and especially for the robustness of multiplex networks against random failures and targeted attacks. We show that these relations lead to fundamentally new behaviors, which emphasizes the importance to consider organizational principles of multiplex networks beyond degree correlations in future research.
Towards a democratic, scalable, and sustainable digital futureKolja Kleineberg
The document discusses the need for a democratic, scalable, and sustainable digital future. It suggests that digital diversity is possible but fragile. Research shows that routing performance can be improved by using multiple networks simultaneously if they exhibit geometric correlations between node coordinates. Incentives like "Social Bitcoin" could sustain a diverse, decentralized digital world by rewarding users for routing information. The goal is for self-organization of the digital world to create a desirable future with robust digital diversity and efficient search/navigation.
Is bigger always better? How local online social networks can outperform glob...Kolja Kleineberg
The overwhelming success of online social networks, the key actors in the cosmos of the Web
2.0, has reshaped human interactions on a worldwide scale. To help understand the fundamental
mechanisms which determine the fate of online social networks at the system level, we describe the
digital world as a complex ecosystem of interacting networks. In this paper, we discuss the impact
of heterogeneity in network fitnesses induced by competition between an international network,
such as Facebook, and local services.To this end, we construct a 1:1000 scale model of the digital
world, consisting of the 80 countries with the most Internet users. We show how inter-country social
ties induce increased fitness of the international network. Under certain conditions, this leads to
the extinction of local networks; whereas under different conditions, local networks can persist and
even dominate the international network completely. These findings provide new insights into the
possibilities for preserving digital diversity.
Hidden geometric correlations in real multiplex networksKolja Kleineberg
Read the paper at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3812.html
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Collective navigation of complex networks: Participatory greedy routingKolja Kleineberg
Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.
Geometric correlations in multiplexes and how they make them more robustKolja Kleineberg
This document discusses research on the structure and dynamics of multiplex networks. It begins by introducing the concept of multiplex networks, which have the same nodes existing across different network layers. It then discusses how degree correlations and geometric correlations between the positions of nodes in the hidden metric spaces underlying different network layers have been found in real multiplex systems. The document explores how these geometric correlations allow for applications like better identifying communities of nodes, improved link prediction between layers, and more efficient navigation across the network layers.
Ecology 2.0: Coexistence and domination among interacting networksKolja Kleineberg
The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of these services for the first time has allowed researchers to quantify large-scale social patterns. However, the mechanisms that determine the fate of networks at a system level are still poorly understood. For instance, the simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.
The Hidden Geometry of Multiplex Networks @ Next Generation Network Analytics Kolja Kleineberg
The document summarizes research on the hidden geometry of multiplex networks. It finds that real-world multiplex networks often have correlated geometric properties between network layers, with nodes maintaining similar radial and angular coordinates. This has implications like communities of nodes being similar across layers and hyperbolic distance in one layer predicting connections in another. A geometric multiplex model is introduced to generate realistic multiplex networks with tunable geometric correlations between layers.
Geometric correlations mitigate the extreme vulnerability of multiplex networ...Kolja Kleineberg
The document discusses how geometric correlations between layers in multiplex networks can mitigate their vulnerability to targeted attacks. It finds that while degree correlations provide some robustness to random failures, they do not prevent catastrophic cascades under targeted attacks. However, geometric or similarity correlations, which place similar nodes close together in an underlying metric space representing each layer, can significantly increase robustness to targeted attacks. This effect is demonstrated through a model incorporating such correlations, as well as analyses of real-world multiplex networks that exhibit stronger geometric correlations.
A Proposed Algorithm to Detect the Largest Community Based On Depth LevelEswar Publications
The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
This document summarizes two presentations about community detection in social media networks. The first presentation discusses using edge content, like image tags, to help identify communities in networks. The second focuses on leveraging interaction intensities on Twitter to detect communities that form around certain events over time. Both aim to improve on traditional methods that only consider network structure.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Distribution of maximal clique size of theIJCNCJournal
Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of
the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category
of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the
vertices with respect to maximal clique size and node degree.
Community Detection in Social Networks: A Brief OverviewSatyaki Sikdar
The document provides an overview of community detection in social networks. It discusses that networks are found everywhere where there are interactions between actors. It then motivates the importance of detecting communities by explaining that communities are groups of nodes that likely share properties and roles. Detecting communities has applications like improving recommendation systems and parallel computing. It also justifies the existence of communities in real networks using the concept of homophily where similar actors tend to connect. The document then discusses different approaches to detecting communities including Girvan-Newman algorithm based on edge betweenness and Louvain method which uses greedy modularity optimization.
LCF is a temporal approach to link prediction in dynamic social networks. It proposes a new predictor called Latest Common Friend (LCF) that incorporates temporal aspects. Social networks are modeled as sequences of snapshots over time periods. Each edge is assigned a weight based on timestamp. LCF score for node pairs is the cumulative weight of their common friends, giving more weight to friends with later timestamps. LCF outperforms traditional predictors like Common Neighbor, Adamic-Adar and Jaccard coefficient on 8 real-world dynamic network datasets based on average AUC scores. Modeling networks temporally and weighting edges by timestamp allows LCF to better predict future links in dynamic social networks.
MODELING SOCIAL GAUSS-MARKOV MOBILITY FOR OPPORTUNISTIC NETWORK csandit
Mobility is attracting more and more interests due to its importance for data forwarding
mechanisms in many networks such as mobile opportunistic network. In everyday life mobile
nodes are often carried by human. Thus, mobile nodes’ mobility pattern is inevitable affected by
human social character. This paper presents a novel mobility model (HNGM) which combines
social character and Gauss-Markov process together. The performance analysis on this
mobility model is given and one famous and widely used mobility model (RWP) is chosen to
make comparison..
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
1. The document discusses a proposed technique called Fuzzy Based Improved Mutual Friend Crawling (Fmfc) for crawling online social networks. It aims to reduce bias introduced by the time taken for crawling the whole network.
2. The technique crawls all users within the same community first before moving to the next community, allowing researchers to selectively obtain users belonging to the same community. This is compared to existing mutual friend crawling.
3. The paper also provides a literature review of existing crawling techniques and studies of complex network properties relevant to community detection in networks. Future work in overlapping communities and performance evaluation on very large networks is discussed.
Interpretation of the biological knowledge using networks approachElena Sügis
This document discusses using biological networks to analyze and interpret biological knowledge. It begins with an overview of networks as tools to reduce complexity and integrate data. Key properties of networks are described, including nodes, edges, degree distribution, clustering coefficient, and centrality measures. Methods for analyzing networks like community detection and network motifs are also covered. The document emphasizes that biological networks must be analyzed and interpreted based on their properties and by mapping relevant biological data to provide meaningful insights.
This document presents a mathematical framework for analyzing systems of interacting networks. The key points are:
1) The framework allows calculating the percolation threshold and component size distributions for systems of l interacting networks, taking into account connectivity both within and between networks.
2) Exact expressions are derived for the percolation threshold and applied to different degree distributions for two interacting networks.
3) The framework is applied to real-world systems involving communications networks and software networks to better understand their structure and function.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Complex Networks Analysis @ Universita Roma TreMatteo Moci
This document discusses complex networks and their analysis. It provides a brief history of network analysis starting in the 18th century with Euler's work on the Seven Bridges of Königsberg problem. It then covers key topics like different types of networks, graph modeling approaches, measures to analyze networks, and applications of network analysis to domains like the web, social networks, and disease spreading. The document emphasizes that understanding network structure and interactions is important for studying complex systems and influences within networks.
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
Towards a democratic, scalable, and sustainable digital future (a complex sys...Kolja Kleineberg
The document discusses a complex systems perspective on achieving a democratic, scalable and sustainable digital future. It summarizes research showing that digital diversity is possible but fragile, and that routing performance improves when individuals are active across multiple networks. The key conclusion is that an appropriate incentive system using cryptocurrency to reward routing could help sustain digital diversity, increase routing performance, and lead to a robust decentralized digital world.
The document provides an overview of a tutorial on network analysis and the law given by Daniel Martin Katz and Michael J. Bommarito II. It discusses Katz's background in law and network science. The tutorial covers an introduction to network analysis including key concepts like nodes, edges, degree distributions and more. It also discusses applications of network analysis to law including legal elites, diffusion of legal ideas, and judicial citation networks. Advanced topics like community detection algorithms are also outlined.
Sheldon Renan's presentation at eComm 2008eComm2008
The document discusses the concept of "netness", which refers to the emerging state of ubiquitous connectivity where lives and systems are increasingly interconnected. As connectivity becomes more prevalent, networks transform into fields and lives become more entangled. This shift represents a fourth state of connectivity beyond being loosely, closely or embedded connected. When all things can connect, safety, capability and opportunity increase, making connectivity vital for optimizing products, business models and governance. Moving forward, further study of connectivity's value and focus on pervasive networks is needed.
The document discusses networks and network theory. It defines what a network is and provides examples of networks in nature, society, and technology. It also discusses key network concepts like nodes, edges, average path length, clustering coefficients, and different types of networks including random, lattice, and small-world networks. Power laws and scale-free networks are also covered.
Geometric correlations mitigate the extreme vulnerability of multiplex networ...Kolja Kleineberg
The document discusses how geometric correlations between layers in multiplex networks can mitigate their vulnerability to targeted attacks. It finds that while degree correlations provide some robustness to random failures, they do not prevent catastrophic cascades under targeted attacks. However, geometric or similarity correlations, which place similar nodes close together in an underlying metric space representing each layer, can significantly increase robustness to targeted attacks. This effect is demonstrated through a model incorporating such correlations, as well as analyses of real-world multiplex networks that exhibit stronger geometric correlations.
A Proposed Algorithm to Detect the Largest Community Based On Depth LevelEswar Publications
The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
This document summarizes two presentations about community detection in social media networks. The first presentation discusses using edge content, like image tags, to help identify communities in networks. The second focuses on leveraging interaction intensities on Twitter to detect communities that form around certain events over time. Both aim to improve on traditional methods that only consider network structure.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Distribution of maximal clique size of theIJCNCJournal
Our primary objective in this paper is to study the distribution of the maximal clique size of the vertices in complex networks. We define the maximal clique size for a vertex as the maximum size of the clique that the vertex is part of and such a clique need not be the maximum size clique for the entire network. We determine the maximal clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of
the maximal clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the maximal clique size of the vertices is observed to be in-variant and to be very close to that of the maximum clique size for the entire network graph as the regular network is transformed to a small-world network. The second category
of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the maximal clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the maximal clique size and clustering coefficient as well as analyze the assortativity index of the
vertices with respect to maximal clique size and node degree.
Community Detection in Social Networks: A Brief OverviewSatyaki Sikdar
The document provides an overview of community detection in social networks. It discusses that networks are found everywhere where there are interactions between actors. It then motivates the importance of detecting communities by explaining that communities are groups of nodes that likely share properties and roles. Detecting communities has applications like improving recommendation systems and parallel computing. It also justifies the existence of communities in real networks using the concept of homophily where similar actors tend to connect. The document then discusses different approaches to detecting communities including Girvan-Newman algorithm based on edge betweenness and Louvain method which uses greedy modularity optimization.
LCF is a temporal approach to link prediction in dynamic social networks. It proposes a new predictor called Latest Common Friend (LCF) that incorporates temporal aspects. Social networks are modeled as sequences of snapshots over time periods. Each edge is assigned a weight based on timestamp. LCF score for node pairs is the cumulative weight of their common friends, giving more weight to friends with later timestamps. LCF outperforms traditional predictors like Common Neighbor, Adamic-Adar and Jaccard coefficient on 8 real-world dynamic network datasets based on average AUC scores. Modeling networks temporally and weighting edges by timestamp allows LCF to better predict future links in dynamic social networks.
MODELING SOCIAL GAUSS-MARKOV MOBILITY FOR OPPORTUNISTIC NETWORK csandit
Mobility is attracting more and more interests due to its importance for data forwarding
mechanisms in many networks such as mobile opportunistic network. In everyday life mobile
nodes are often carried by human. Thus, mobile nodes’ mobility pattern is inevitable affected by
human social character. This paper presents a novel mobility model (HNGM) which combines
social character and Gauss-Markov process together. The performance analysis on this
mobility model is given and one famous and widely used mobility model (RWP) is chosen to
make comparison..
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
1. The document discusses a proposed technique called Fuzzy Based Improved Mutual Friend Crawling (Fmfc) for crawling online social networks. It aims to reduce bias introduced by the time taken for crawling the whole network.
2. The technique crawls all users within the same community first before moving to the next community, allowing researchers to selectively obtain users belonging to the same community. This is compared to existing mutual friend crawling.
3. The paper also provides a literature review of existing crawling techniques and studies of complex network properties relevant to community detection in networks. Future work in overlapping communities and performance evaluation on very large networks is discussed.
Interpretation of the biological knowledge using networks approachElena Sügis
This document discusses using biological networks to analyze and interpret biological knowledge. It begins with an overview of networks as tools to reduce complexity and integrate data. Key properties of networks are described, including nodes, edges, degree distribution, clustering coefficient, and centrality measures. Methods for analyzing networks like community detection and network motifs are also covered. The document emphasizes that biological networks must be analyzed and interpreted based on their properties and by mapping relevant biological data to provide meaningful insights.
This document presents a mathematical framework for analyzing systems of interacting networks. The key points are:
1) The framework allows calculating the percolation threshold and component size distributions for systems of l interacting networks, taking into account connectivity both within and between networks.
2) Exact expressions are derived for the percolation threshold and applied to different degree distributions for two interacting networks.
3) The framework is applied to real-world systems involving communications networks and software networks to better understand their structure and function.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Complex Networks Analysis @ Universita Roma TreMatteo Moci
This document discusses complex networks and their analysis. It provides a brief history of network analysis starting in the 18th century with Euler's work on the Seven Bridges of Königsberg problem. It then covers key topics like different types of networks, graph modeling approaches, measures to analyze networks, and applications of network analysis to domains like the web, social networks, and disease spreading. The document emphasizes that understanding network structure and interactions is important for studying complex systems and influences within networks.
This document discusses how rumors spread quickly through social networks. It simulates a simple rumor spreading process on real-world social networks like Twitter and Orkut as well as theoretical network models. The results show that rumors spread much faster in the structures of actual social networks and preferential attachment networks than in random or complete networks. Specifically, a rumor reaching 45.6 million Twitter users within 8 rounds of communication.
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
Towards a democratic, scalable, and sustainable digital future (a complex sys...Kolja Kleineberg
The document discusses a complex systems perspective on achieving a democratic, scalable and sustainable digital future. It summarizes research showing that digital diversity is possible but fragile, and that routing performance improves when individuals are active across multiple networks. The key conclusion is that an appropriate incentive system using cryptocurrency to reward routing could help sustain digital diversity, increase routing performance, and lead to a robust decentralized digital world.
The document provides an overview of a tutorial on network analysis and the law given by Daniel Martin Katz and Michael J. Bommarito II. It discusses Katz's background in law and network science. The tutorial covers an introduction to network analysis including key concepts like nodes, edges, degree distributions and more. It also discusses applications of network analysis to law including legal elites, diffusion of legal ideas, and judicial citation networks. Advanced topics like community detection algorithms are also outlined.
Sheldon Renan's presentation at eComm 2008eComm2008
The document discusses the concept of "netness", which refers to the emerging state of ubiquitous connectivity where lives and systems are increasingly interconnected. As connectivity becomes more prevalent, networks transform into fields and lives become more entangled. This shift represents a fourth state of connectivity beyond being loosely, closely or embedded connected. When all things can connect, safety, capability and opportunity increase, making connectivity vital for optimizing products, business models and governance. Moving forward, further study of connectivity's value and focus on pervasive networks is needed.
The document discusses networks and network theory. It defines what a network is and provides examples of networks in nature, society, and technology. It also discusses key network concepts like nodes, edges, average path length, clustering coefficients, and different types of networks including random, lattice, and small-world networks. Power laws and scale-free networks are also covered.
The document provides an overview of the Open System Interconnection Model (OSI) and the Telecommunications Act of 1996. The OSI is a 7-layer model that defines a framework for network communication. The layers include the physical, data link, network, transport, session, presentation and application layers. The Telecommunications Act of 1996 overhauled US communications law and allowed any communications firm to compete in the market through fair practices. It impacted telephone, cable, broadcast and educational services. The Act also included provisions for controlling television content and preventing undue concentration in media ownership.
The overwhelming success of online social networks, the key actors in the cosmos of the Web 2.0, has reshaped human interactions on a worldwide scale. To understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we discuss the impact of heterogeneity in network intrinsic fitnesses induced by the competition between an international network, like Facebook, and local services. To this end, we construct a 1:1000 scale model of the digital world enclosing the 80 countries with most Internet users. We show how inter-country social ties induce an increased intrinsic fitness of the international network. Under certain conditions this leads to the extinction of local networks whereas under different conditions local networks can persist and even dominate the international network completely. These findings provide new insights into the possibilities to preserve digital diversity.
This document summarizes a lecture on network science given by Madhav Marathe at Lawrence Livermore National Laboratory in December 2010. It provides an overview of network science, including definitions of networks and their unique properties. It also discusses mathematical and computational approaches to modeling complex networks and applications to infrastructure planning, energy systems, and national security. The lecture acknowledges prior work that contributed to its material from various researchers and textbooks.
Mining and analyzing social media part 2 - hicss47 tutorial - dave kingDave King
This document provides an overview and introduction to social network analysis metrics and techniques for analyzing social media data. It discusses common social network analysis concepts like degrees of separation, centrality measures to identify influential users, and cohesion measures to understand how well connected a network is. It also presents examples analyzing Facebook networks and techniques for identifying cohesive subgroups within large social networks. The document demonstrates how social network analysis can be used to systematically study relationships and information flow within social systems.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
This document provides an introduction to social network analysis. It discusses how network analysis allows us to understand social connections and positions. There are two key mechanisms through which networks can impact outcomes: connections, where networks matter because of what flows through them, and positions, where networks capture roles and social exchange. Network analysis provides tools to empirically study patterns of social structure by mapping relationships between actors.
COMMUNICATIONS OF THE ACM November 2004Vol. 47, No. 11 15.docxmonicafrancis71118
COMMUNICATIONS OF THE ACM November 2004/Vol. 47, No. 11 15
N
etworks are hot. The
Internet has made it pos-
sible to observe and mea-
sure linkages
representing relationships of
all kinds. We now recognize
networks everywhere: air
traffic, banking, chemical
bonds, data communications,
ecosystems, finite element
grids, fractals, interstate
highways, journal citations,
material structures, nervous
systems, oil pipelines, orga-
nizational networks, power
grids, social structures, trans-
portation, voice communica-
tion, water supply, Web
URLs, and more.
Several fields are collabo-
rating on the development of
network theory, measurement,
and mapping: mathematics
(graph theory), sociology (net-
works of influence and communi-
cation), computing (Internet), and
business (organizational net-
works). This convergence has pro-
duced useful results for risk
assessment and reduction in com-
plex infrastructure networks,
attacking and defending networks,
protecting against network con-
nectivity failures, operating busi-
nesses, spreading epidemics
(pathogens as well as computer
viruses), and spreading innova-
tion. Here, I will survey the fun-
damental laws of networks that
enable these results.
Defining a Network
A network is usually defined as a
set of nodes and links. The nodes
represent entities such as persons,
machines, molecules, documents,
or businesses; the links represent
relationships between pairs of
entities. A link can be directed
(one-way relationship) or undi-
rected (mutual relationship). A
hop is a transition from one node
to another across a single link
separating them. A path is a series
of hops. Networks are very gen-
eral: they can represent any kind
of relation among entities.
Some common network
topologies (interconnection pat-
terns) have their own names:
clique or island (a connected sub-
network that may be isolated
from other cliques), hierarchical
network (tree structured), hub-
and-spoke network (a special
node, the hub, connected directly
to every other node), and multi-
hub network (several hubs con-
nected directly to many nodes).
Some network topologies are
planned, such as the electric grid,
the interstate highway system, or
Network Laws
M
IC
H
A
EL
S
LO
A
N
Peter J. Denning
Many networks, physical and social, are complex and scale-invariant.
This has important implications from the spread of epidemics and
innovations to protection from attack.
The Profession of IT
16 November 2004/Vol. 47, No. 11 COMMUNICATIONS OF THE ACM
the air traffic system; others are
unplanned. In his seminal papers
about the Internet, Paul Baran
proposed that a planned, distrib-
uted network would be more
resilient to failures than a hub-
and-spoke network.
A host of physical systems eas-
ily fit a network model. Perhaps
less obvious is that human social
networks also fit the model. The
individuals of an organization are
linked by their relationships—
who emails whom, who seeks
advice from whom, or who influ-
ences w.
Network science is an interdisciplinary field that studies complex networks. It draws on theories from mathematics, physics, computer science, statistics, and sociology. The document provides an introduction to network science and outlines topics including network analysis, visualization, and business applications. It also summarizes the history and development of network science as an academic field.
The Network Effects Bible is a comprehensive collection of terms and insights related to network effects all in one place. Produced by James Currier & the NFX team (www.nfx.com), an early-stage venture capital firm started by entrepreneurs who've built 10 network effect companies with more than $10 billion in exits across multiple industries and geographies.
Read the full Network Effects Bible at: https://www.nfx.com/post/network-effects-bible/
Follow us on Twitter @NFX
The document discusses complex networks and their application to smart cities. It begins by defining complex networks and describing different types of real-world networks, including social, information, technological, and biological networks. It then discusses Euler's graph theory and its application to modeling city utility networks. The document introduces the concept of a smart city and uses call detail record data from mobile networks as an example of how such data can provide urban information. It concludes by suggesting readers are ready to become linked within these complex networks.
This document summarizes a panel discussion on internet security and privacy ten years in the future. The panel will discuss key threats and elements that may shape security and privacy over the next decade. They will consider how approaches to security and privacy may need to change as internet technologies evolve. The panel will also explore new approaches like information-centric networking and homomorphic encryption that could potentially solve future problems. Finally, the discussion will address whether security and privacy will become more antagonistic, requiring harder trade-offs, or more cooperative over time to allow more comprehensive solutions.
This document discusses diffusion and peer influence through networks. It begins by defining diffusion and compartment models used to model disease spread. It then discusses how network structure, including topology, timing of connections, and clustering, can impact diffusion compared to random mixing. Key network features that influence diffusion speed and reach include distance between actors, number of alternate paths, presence of highly connected "star" nodes, and assortative mixing. The document concludes by exploring how different degree distributions in emergent low-density networks can impact the formation of large connected components.
The document discusses network diffusion and peer influence. It begins by defining diffusion and compartment models used to model disease spread. It then discusses how network structure, including topology, timing of connections, and structural transmission, can impact diffusion. Simulation is proposed to test how network features like distance, clustering, redundancy, and high-degree nodes influence spread. The relationships between contact networks, exposure networks based on timing, and actual transmission networks are also introduced.
Can network science be taught with a gamified approach?
These are the slides i use for an introductory lesson about network science basics. The teaching methodology is inductive.
First part (pp 1-42)
The five main topics (nodes and links, centrality, in-degree and out-degree, diffusion through networks, degrees of separation) are presented through a concrete case. Students have a game to answer, in presence and/or on line, for each subject. This interactive phase activate their interest and curiosity.
Second part (pp 43-58)
Each game is recovered, and the experience of the game is explained and connected with the relative concept of network science.
Third part (59-68)
At the end there is a meta-reflection the gamified experience, talking about the “what” (contents about network complexity) and the “how” (modalities to sustain engagement).
The document discusses how the internet and connectivity have changed the way people live, work and learn. It notes that the complexity of the internet today is comparable to the human brain, but will exceed it greatly in the future. It also discusses the evolution of the web from linking computers, to linking content, to linking people through social networking. Living and learning are now done in a highly networked world.
From the Evolution of Online Social Networks to Digital Ecology in a NutshellKolja Kleineberg
The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of these services for the first time has allowed researchers to quantify large-scale social patterns. However, the mechanisms that determine the fate of networks at a system level are still poorly understood. For instance, the simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.
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Catastrophic instabilities in interacting networks and possible remediesKolja Kleineberg
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Towards controlling evolutionary dynamics through network geometry: some very...Kolja Kleineberg
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Interplay between social influence and competitive strategical games in multi...Kolja Kleineberg
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The document discusses catastrophic instabilities that can occur in interacting networks and possible remedies. It summarizes research showing that coexistence among competing online social networks is possible but fragile. It proposes that providing incentives for users to route information across multiple networks could help sustain digital diversity. The document also examines how hidden geometric correlations in real-world multiplex networks can mitigate vulnerabilities to failures or attacks.
The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of these services for the first time has allowed researchers to quantify large-scale social patterns. However, the mechanisms that determine the fate of networks at a system level are still poorly understood. For instance, the simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
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Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
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Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
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(Digital) networks and the science of complex systems
1. (Digital) networks and
the science of complex systems
Kaj Kolja Kleineberg | kkleineberg@ethz.ch
@KoljaKleineberg | koljakleineberg.wordpress.com
2. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Agenda:
From complex systems to digital networks
1. Networks and complex systems
2. The power of digital networks
3. Sustaining digital diversity
4. Summary
2
4. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Swap the positions of the black and white knights
4Credits: Marian Boguna
5. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Network approach makes the problem really easy
5Credits: Marian Boguna
6. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Network approach makes the problem really easy
5Credits: Marian Boguna
7. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Network approach makes the problem really easy
5Credits: Marian Boguna
8. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Network approach makes the problem really easy
5Credits: Marian Boguna
9. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A chess riddle:
Network approach makes the problem really easy
5Credits: Marian Boguna
10. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Seven bridges of Königsberg:
crossing each bridge only once
The city of Königsberg was set on both sides of the Pregel River,
and included two large islands which were connected to each
other, or to the two mainland portions of the city, by seven
bridges. The problem was to devise a walk through the city that
would cross each of those bridges once and only once.
6
11. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Seven bridges of Königsberg:
the foundation of graph theory
https://en.wikipedia.org/wiki/Seven_Bridges_of_Königsberg
Is it possible to cross all bridges only once?
7
12. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Seven bridges of Königsberg:
the foundation of graph theory
https://en.wikipedia.org/wiki/Seven_Bridges_of_Königsberg
Is it possible to cross all bridges only once?
Whenever one enters a node by a bridge, one leaves the node by a bridge
The number of times one enters a non-terminal node equals the number of
times one leaves it → even number except for start endpoint
However, all nodes have uneven number of bridges attached →
Contradiction
7
13. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
complex networks
8
14. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
complex networks
Complex networks are maps of complex
systems.
8
15. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
What is a complex system?
9
16. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Definition:
What is a complex system?
...the whole is more than the sum of the parts, [...] in
the sense that, given the properties of the parts and the
laws of their interaction, it is not a trivial matter to infer
the properties of the whole.
Herbert A. Simon
10
17. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Definition:
What is a complex system?
...the whole is more than the sum of the parts, [...] in
the sense that, given the properties of the parts and the
laws of their interaction, it is not a trivial matter to infer
the properties of the whole.
Herbert A. Simon
Large number
of components
10
18. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Definition:
What is a complex system?
...the whole is more than the sum of the parts, [...] in
the sense that, given the properties of the parts and the
laws of their interaction, it is not a trivial matter to infer
the properties of the whole.
Herbert A. Simon
Large number
of components
Nonlinear
interactions
10
19. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Definition:
What is a complex system?
...the whole is more than the sum of the parts, [...] in
the sense that, given the properties of the parts and the
laws of their interaction, it is not a trivial matter to infer
the properties of the whole.
Herbert A. Simon
Large number
of components
Nonlinear
interactions
Emergent
behavior
10
20. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
A car is not a complex system
(we call it a difficult system)
11
21. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The Internet is a complex system
(without a central masterplan)
12
22. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The Internet is a complex system:
lack of scale
13Nature, 401:130-131 (1999)
23. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Scale-free property common to many real complex networks
induces a vanishing epidemic threshold
14
24. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Networks allow us to map interactions
and study system-level implications
15Nature Physics 10, 762-767 (2014); Phys. Rev. Lett. 109, 064101 (2013)
25. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Financial networks
and systemic risk (debt rank)
16
26. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Similar procedure allows Google
to rank websites (page rank)
17
28. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Facebook likes predict your personality
better than your spouse
19
29. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Social messages can influence
real world voting behavior
20
30. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Search algorithms can also change
real world voting behavior
21
31. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Algorithms can change how you feel
by changing your Facebook timeline
22
32. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
What if all this power
is in one hand?
23
34. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Model contains spreading dynamics
and the influence of mass media
Online social
network layer
Traditional contact
network layer
Active
Online offline
Passive
Online offline
Susceptible
Only offline
25
35. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Model contains spreading dynamics
and the influence of mass media
Online social
network layer
Traditional contact
network layer
Active
Online offline
Passive
Online offline
Susceptible
Only offline
Mass media activation Viral activation
Deactivation Viral reactivation
25
36.
37. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The evolution of isolated online social networks:
insights from a system-level description
Underlying social structure
determines topological
evolution
PRX 4, 031046, 2014
27
38. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The evolution of isolated online social networks:
insights from a system-level description
Underlying social structure
determines topological
evolution
Balance
of viral and mass media
influence
PRX 4, 031046, 2014
27
39. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The evolution of isolated online social networks:
insights from a system-level description
Underlying social structure
determines topological
evolution
Balance
of viral and mass media
influence
Survival and death
of networks
PRX 4, 031046, 2014
27
40. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
The evolution of isolated online social networks:
insights from a system-level description
Underlying social structure
determines topological
evolution
Balance
of viral and mass media
influence
Survival and death
of networks
Weak ties
have higher transmissibility
PRX 4, 031046, 2014
27
41. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Digital ecosystem is formed by multiple networks
competing for the attention of individuals
OSN 2
OSN 1
Underl.
network
Active
Passive
Susceptible
Partial
states}
Virality share
distribution
between OSNs
λi = ωi(ρa)λ
Rich-get-richer
more active
networks obtain
higher share
Here: ωi = [ρa
i ]σ/
∑
j[ρa
j]σ
σ: activity affinity
28Sci. Rep. 5, 10268 (2015)
42. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Coexistence (diversity) is possible but fragile:
once lost, it cannot be recovered
Stable
Unstable
0.50 0.75 1.00 1.25 1.50
0.00
0.25
0.50
0.75
0.00
0.25
0.50
0.75
Bifurcation diagram
σ
ρ1,2
a
29Sci. Rep. 5, 10268 (2015)
43. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Coexistence (diversity) is possible but fragile:
once lost, it cannot be recovered
Stable
Unstable
0.50 0.75 1.00 1.25 1.50
0.00
0.25
0.50
0.75
0.00
0.25
0.50
0.75
Bifurcation diagram
σ
ρ1,2
a
Coexistence
despite rich-get-richer
29Sci. Rep. 5, 10268 (2015)
44. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Coexistence (diversity) is possible but fragile:
once lost, it cannot be recovered
Stable
Unstable
0.50 0.75 1.00 1.25 1.50
0.00
0.25
0.50
0.75
0.00
0.25
0.50
0.75
Bifurcation diagram
σ
ρ1,2
a
Coexistence
despite rich-get-richer
Digital diversity
is irreversible
29Sci. Rep. 5, 10268 (2015)
45. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Network of multi-layer networks
represents global digital ecology
Active
Passive
Susceptible
Partial
states}
Local
network
Global
network
Effective activity
30
46.
47. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Digital networks can coexist
but coexistence is fragile
Coexistence
despite rich-get-richer
Sci. Rep. 5, 10268 (2015) • Sci. Rep. 6, 25116 (2016)
32
48. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Digital networks can coexist
but coexistence is fragile
Coexistence
despite rich-get-richer
Diversity
is fragile
Sci. Rep. 5, 10268 (2015) • Sci. Rep. 6, 25116 (2016)
32
49. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Digital networks can coexist
but coexistence is fragile
Coexistence
despite rich-get-richer
Diversity
is fragile
Moderate
digital diversity observed
Sci. Rep. 5, 10268 (2015) • Sci. Rep. 6, 25116 (2016)
32
50. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Digital networks can coexist
but coexistence is fragile
Coexistence
despite rich-get-richer
Diversity
is fragile
Moderate
digital diversity observed
Facebook
takes over local networks
Sci. Rep. 5, 10268 (2015) • Sci. Rep. 6, 25116 (2016)
32
51. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Utility of networks: bottom-up search and navigation
with incentives based on sharing generated value
Navigation
is common function
networks perform
33Nature Communications 1, 62 (2010) • Scientific Reports 7, 2897 (2017)
52. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Utility of networks: bottom-up search and navigation
with incentives based on sharing generated value
Navigation
is common function
networks perform
Individuals
perform routing
instead of service
providers
33Nature Communications 1, 62 (2010) • Scientific Reports 7, 2897 (2017)
53. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Utility of networks: bottom-up search and navigation
with incentives based on sharing generated value
Navigation
is common function
networks perform
Individuals
perform routing
instead of service
providers
Payoff
for successful
deliveries
33Nature Communications 1, 62 (2010) • Scientific Reports 7, 2897 (2017)
54.
55. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Navigation works better with
more active networks
35Nature Physics 12, 1076–1081 (2016)
56. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
Appropriate incentives could create a feedback loop
building on self-organization of complex systems
Qualified Money
t
o
route
activein
m
any networks
Optim
izes strategy:
Inc
entive
Sustains
digital dive
rsity
Increasesro
uting
perform
a
nce
Exchangeable
Social Bitcoin
+Reputation
Feedback loop
can overcome fragility
Digital diversity
can become robust
sustainable
36Eur. Phys. J. Spec. Top. 225: 3231 (2016)
58. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
(Digital) networks
and the science of complex systems
Complex systems:
emergent behavior, no central
control
38
59. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
(Digital) networks
and the science of complex systems
Complex systems:
emergent behavior, no central
control
Networks
are system level maps of
interactions
38
60. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
(Digital) networks
and the science of complex systems
Complex systems:
emergent behavior, no central
control
Networks
are system level maps of
interactions
Digital networks
are extremely powerful
38
61. Networks and complex systems The power of digital networks Sustaining digital diversity Summary
(Digital) networks
and the science of complex systems
Complex systems:
emergent behavior, no central
control
Networks
are system level maps of
interactions
Digital networks
are extremely powerful
Digital diversity
could be sustained with
incentives
38
62. Kaj Kolja Kleineberg:
• kkleineberg@ethz.ch
• @KoljaKleineberg
• koljakleineberg.wordpress.com
ETH Zurich:
• Computational Social Science Group
• http://www.coss.ethz.ch
63. Kaj Kolja Kleineberg:
• kkleineberg@ethz.ch
• @KoljaKleineberg ← Slides updates
• koljakleineberg.wordpress.com
ETH Zurich:
• Computational Social Science Group
• http://www.coss.ethz.ch
64. Kaj Kolja Kleineberg:
• kkleineberg@ethz.ch
• @KoljaKleineberg ← Slides updates
• koljakleineberg.wordpress.com ← References
ETH Zurich:
• Computational Social Science Group
• http://www.coss.ethz.ch