USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
An overview of Web research areas of interest to social scientists presented at Brunel University 3 March 2010, including an overview of my attempts to understand social influence online for my PhD thesis (http://alekskrotoski.com/tags/phd). includes general findings and an overview of the themes discussed in BBC2's Virtual Revolution series.
AN INTRODUCTION TO WIRELESS MOBILE SOCIAL NETWORKING IN OPPORTUNISTIC COMMUNI...ijdpsjournal
Next generation networks will certainly face requesting access from different parts of the
network. The heterogeneity of communication and application software’s changing situations in
the environment, from the users, the operators, the business requirements as well as the
technologies. Users will be more and more mobile, protocols, etc. will increase and render the
network more complex to manage. Opportunistic communication has emerged as a new
communication paradigm to cope with these problems. Opportunistic networksexploits the
variation of channel conditions, provides an additional degree of freedom in the time domain and
increase network performance.The limited spectrum and the inefficiency in the spectrum usage
require such a new communication to exploit the existing wireless spectrum opportunistically by
allocation of spectrum based on best opportunity among all possibilities
Social Software and Community Information SystemsRalf Klamma
Social Software links social entities on the Internet. With this term we label new communication and collaboration media like wikis, blogs, social bookmarking but also traditional media supporting communities of practice. Scientific and professional communities challenge information systems engineering with high demands on traceable and secured collaboration and processing of scientific data. Flexibility, adaptation, interoperability are only a few requirements to mention.
With the advent of international standards XML-based standards like MPEG-7 for the handling of complex multimedia metadata and service oriented architectures engineers and community facilitators can create more generic services for the many communities with diverse but professional needs. Therefore, communities have to be incorporated in the community information systems engineering process.
In the talk we present a new reflective information system architecture called ATLAS offering self observation mechanisms for the establishment of a community-centered learning and improvement process for social software.
COMMUNITY DETECTION USING INTER CONTACT TIME AND SOCIAL CHARACTERISTICS BASED...ijasuc
Delay Tolerant Networks (DTNs) where the node connectivity is opportunistic and end-to-end path between
any pair of source and destination is not guaranteed most of the time. Hence the messages are transferred
from source to destination via intermediate nodes on hop to hop basis using store-carry-forward paradigm.
Due to quick advancement in hand held devices such as smart phone and laptop with support of wireless
communication interface carried by human being, it is possible in coming days to use DTNs for message
dissemination without setting up infrastructure. The routing task becomes challenging in DTNs due to
intermittent network connectivity and the connection opportunity arises only when node comes in
transmission range of each other. The performance of the routing protocols depend on the selection of
appropriate relay node which can deliver the message to final destination in case of source and destination
do not meet at all. Many social characteristics are exhibited by the human being like friendship,
community, similarity and centrality which can be exploited by the routing protocol in order to take the
forwarding decisions. Literature shows that by using these characteristics, the performance of DTN routing
protocols have been improved in terms of delivery probability. The existing routing schemes used
community detection using aggregated contact duration and contact frequency which does not change over
the time period. We propose community detection through Inter Contact Time (ICT) between node pair
using power law distribution where the members of community are added and removed dynamically. We
also considered single copy of each message in entire network to reduce the network overhead. The
proposed routing protocol named Social Based Single Copy Routing (SBSCR) selects the suitable relay
node from the community members only based on the social metrics such as similarity and friendship
together. ICTs show power law nature in human mobility which is used to detect the community structure at
each node. A node maintains its own community and social metrics such as similarity and friendship with
other nodes. Whenever node has to select the relay node then it selects from its community with higher
value of social metric. The simulations are conducted using ONE simulator on the real traces of campus
and conference environments. SBSCR is compared with existing schemes and results show that it
outperforms in terms of delivery probability and delivery delay with comparable overhead ratio.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
This chapter will introduce you to the field of science known as Network Theory and tell you about the major researches that took place since its conceptualization. Since the course in question is social computing the chapter is written in a way to give examples and illustrations which mostly relate to social computing. It also contains theories and information which are mostly related to network theory and have some or no relation to social computing. But the basic purpose of this chapter is to explain Network theory and its applications in the field of social computing.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
An overview of Web research areas of interest to social scientists presented at Brunel University 3 March 2010, including an overview of my attempts to understand social influence online for my PhD thesis (http://alekskrotoski.com/tags/phd). includes general findings and an overview of the themes discussed in BBC2's Virtual Revolution series.
AN INTRODUCTION TO WIRELESS MOBILE SOCIAL NETWORKING IN OPPORTUNISTIC COMMUNI...ijdpsjournal
Next generation networks will certainly face requesting access from different parts of the
network. The heterogeneity of communication and application software’s changing situations in
the environment, from the users, the operators, the business requirements as well as the
technologies. Users will be more and more mobile, protocols, etc. will increase and render the
network more complex to manage. Opportunistic communication has emerged as a new
communication paradigm to cope with these problems. Opportunistic networksexploits the
variation of channel conditions, provides an additional degree of freedom in the time domain and
increase network performance.The limited spectrum and the inefficiency in the spectrum usage
require such a new communication to exploit the existing wireless spectrum opportunistically by
allocation of spectrum based on best opportunity among all possibilities
Social Software and Community Information SystemsRalf Klamma
Social Software links social entities on the Internet. With this term we label new communication and collaboration media like wikis, blogs, social bookmarking but also traditional media supporting communities of practice. Scientific and professional communities challenge information systems engineering with high demands on traceable and secured collaboration and processing of scientific data. Flexibility, adaptation, interoperability are only a few requirements to mention.
With the advent of international standards XML-based standards like MPEG-7 for the handling of complex multimedia metadata and service oriented architectures engineers and community facilitators can create more generic services for the many communities with diverse but professional needs. Therefore, communities have to be incorporated in the community information systems engineering process.
In the talk we present a new reflective information system architecture called ATLAS offering self observation mechanisms for the establishment of a community-centered learning and improvement process for social software.
COMMUNITY DETECTION USING INTER CONTACT TIME AND SOCIAL CHARACTERISTICS BASED...ijasuc
Delay Tolerant Networks (DTNs) where the node connectivity is opportunistic and end-to-end path between
any pair of source and destination is not guaranteed most of the time. Hence the messages are transferred
from source to destination via intermediate nodes on hop to hop basis using store-carry-forward paradigm.
Due to quick advancement in hand held devices such as smart phone and laptop with support of wireless
communication interface carried by human being, it is possible in coming days to use DTNs for message
dissemination without setting up infrastructure. The routing task becomes challenging in DTNs due to
intermittent network connectivity and the connection opportunity arises only when node comes in
transmission range of each other. The performance of the routing protocols depend on the selection of
appropriate relay node which can deliver the message to final destination in case of source and destination
do not meet at all. Many social characteristics are exhibited by the human being like friendship,
community, similarity and centrality which can be exploited by the routing protocol in order to take the
forwarding decisions. Literature shows that by using these characteristics, the performance of DTN routing
protocols have been improved in terms of delivery probability. The existing routing schemes used
community detection using aggregated contact duration and contact frequency which does not change over
the time period. We propose community detection through Inter Contact Time (ICT) between node pair
using power law distribution where the members of community are added and removed dynamically. We
also considered single copy of each message in entire network to reduce the network overhead. The
proposed routing protocol named Social Based Single Copy Routing (SBSCR) selects the suitable relay
node from the community members only based on the social metrics such as similarity and friendship
together. ICTs show power law nature in human mobility which is used to detect the community structure at
each node. A node maintains its own community and social metrics such as similarity and friendship with
other nodes. Whenever node has to select the relay node then it selects from its community with higher
value of social metric. The simulations are conducted using ONE simulator on the real traces of campus
and conference environments. SBSCR is compared with existing schemes and results show that it
outperforms in terms of delivery probability and delivery delay with comparable overhead ratio.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
This chapter will introduce you to the field of science known as Network Theory and tell you about the major researches that took place since its conceptualization. Since the course in question is social computing the chapter is written in a way to give examples and illustrations which mostly relate to social computing. It also contains theories and information which are mostly related to network theory and have some or no relation to social computing. But the basic purpose of this chapter is to explain Network theory and its applications in the field of social computing.
In this project I use a stack of denoising autoencoders to learn low-dimensional
representations of images. These encodings are used as input to a locality sensitive
hashing algorithm to find images similar to a given query image. The results clearly
shows that this approach outperforms basic LSH by far.
Towards a democratic, scalable, and sustainable digital futureKolja Kleineberg
The digital world is changing at an unprecedented pace. Recently, the power of central entities acting like monopolies of information has become extremely large and provides these entities with the possibility to undermine the freedom of society. Hence, we need a democratic vision of the digital future in which individuals are in control of their information instead of a few monopolies. Furthermore, the Internet of Things will lead to an explosion in the number of devices, emphasizing the need to design a scalable digital future. Last but not least, the rapid evolution of the digital environment demands for a robust vision of the digital future to ensure its sustainability. Here, I provide a systems perspective on essential ingredients for such a desirable digital future.
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.
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.
Given at PyDataSV 2014
In machine learning, clustering is a good way to explore your data and pull out patterns and relationships. Scikit-learn has some great clustering functionality, including the k-means clustering algorithm, which is among the easiest to understand. Let's take an in-depth look at k-means clustering and how to use it. This mini-tutorial/talk will cover what sort of problems k-means clustering is good at solving, how the algorithm works, how to choose k, how to tune the algorithm's parameters, and how to implement it on a set of data.
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.
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.
Online social network mining current trends and research issueseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Description and Composition of Bio-Inspired Design Patterns: The Gradient CaseFernandez-Marquez
3rd Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems. Slides of the presentation: Description and Composition of Bio-Inspired Design Patterns: The Gradient Case
The increased capabilities (e.g., processing, storage) of portable devices along with the constant need of users to retrieve and send information have introduced a new form of communication. Users can seamlessly exchange data by means of opportunistic contacts among them and this is what characterizes the opportunistic networks (OppNets). OppNets allow users to communicate even when an end-to-end path may not exist between them.
A trend observed in the last year of opportunistic routing refers to considering social similarity metrics to improve the exchange of data. Social relationships, shared interests, and popularity are examples of such metrics that have been employed successfully: as users interact based on relationships and interests, this information can be used to decide on the best next forwarders of information.
This Thesis work combines the features of today's devices found in the regular urban environment with the current social-awareness trend in the context of opportunistic routing. To achieve this goal, this work was divided into different tasks that map to a set of specific objectives, leading to the following contributions: i) an up-to-date opportunistic routing taxonomy; ii) a universal evaluation framework that aids in devising and testing new routing proposals; iii) three social-aware utility functions that consider the dynamic user behavior and can be easily incorporated to other routing proposals; iv) two opportunistic routing proposals based on the users' daily routines and on the content traversing the network and interest of users in such content; and v) a structure analysis of the social-based network formed based on the approaches devised in this work.
This presentation was given as part of my PhD defense to the Universities of Minho, Aveiro, and Porto, on September 29th, 2014 in University of Aveiro.
For a copy of the thesis: http://copelabs.ulusofona.pt/scicommons/index.php/publications/show/732
Thèse "Community detection in complex network" par Vinh-Loc DAO, lors de la journée Futur & Ruptures du 31 janvier 2019. Une journée scientifique pour présenter l’ensemble des travaux de thèses aboutis portant sur des thématiques prospectives du programme de l’IMT.
E-Learning Social Network Analysis for Social Awareness by Niki LambropoulosNiki Lambropoulos PhD
E-Learning Social Network Analysis for Social Awareness by Niki Lambropoulos
Presentation delivered at the Images of Virtuality Conference
Athens, 23-24 April, 2009
http://www.imagesofvirtuality.org/
A quick presentation about applications of complexity thinking in creativity and collaboration for the December 2009 Research Club brunch. I cover the broad ideas of complex systems design, applications within projects, and the opportunity for collaboration.
For more information on these ideas and the project PDX I Love You check out www.whichlight.com
Similar to Multidimensional Analysis of Complex Networks (20)
Progetto Vincitore del primo H-ack in H-farm
by Chiara Olivieri, Giovanni Trento, Andrea Bazerla, Lino Possamai, Walter Barbagallo, Nicola Ghirardi, Enrico Battistelli, Giovanna Nardini, Alessandro Paolini
http://ghirardinicola.blogspot.it/2013/10/and-winner-is-team-fungo-hackindustry.html
Optimization of Collective Communication in MPICH Lino Possamai
This is a lecture about the paper: "Optimization of Collective Communication in MPICH". Department of Computer Science, University Ca' Foscari of Venice, Italy
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
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Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
1. Motivation Multidimensional Spatial analysis Growth analysis
Multidimensional analysis of complex networks
Possamai Lino
Alma Mater Studiorum Università di Bologna
Università di Padova
Ph.D. Dissertation Defense
February 21st, 2013
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 1/39
2. Motivation Multidimensional Spatial analysis Growth analysis
Publications and conferences list
Plos One 2012 Thi Hoang, Sun, Possamai, JafariAsbagh, Patil, Menczer
Scholarometer: A Social Framework for Analyzing Impact across Disciplines
IPM 2012 Sun, Kaur, Possamai, Menczer
Ambiguous Author Query Detection using Crowdsourced Digital Library Annotations
SocialCom11 2011 Sun, Kaur, Possamai and Menczer
Detecting Ambiguous Author Names in Crowdsourced Scholarly Data
PSB2010 2010 Biasiolo, Forcato, Possamai, Ferrari, Agnelli, Lionetti, Todoerti, Neri, Marchiori et al.
Critical analysis of transcriptional and post-transcriptional regulatory networks in
Multiple Myeloma
Sunbelt2010 2010 Marchiori, Possamai
Telescopic analysis of complex networks
PRIB2009 2009 Forcato, Possamai, Ferrari, Agnelli, Todoerti, Lambertenghi, Bortoluzzi, Marchiori et al.
Reverse Engineering and Critical Analysis of Gene Regulatory Networks
in Multiple Myeloma
(under submission) 2013 Toward an optimized evolution of social networks
(under submission) 2013 Micro-macro analysis of complex networks
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 2/39
4. Motivation Multidimensional Spatial analysis Growth analysis
Domain
A complex system is a network of elements that
interacts in a non-linearly way, resulting in an
overall behavior that is difficult to predict.
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 4/39
5. Motivation Multidimensional Spatial analysis Growth analysis
Domain
A complex system is a network of elements that
interacts in a non-linearly way, resulting in an
overall behavior that is difficult to predict.
The digitalization of every day’s actions allows a
deeper investigation on how persons, computers,
animals, companies etc interact
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 4/39
6. Motivation Multidimensional Spatial analysis Growth analysis
Domain
A complex system is a network of elements that
interacts in a non-linearly way, resulting in an
overall behavior that is difficult to predict.
The digitalization of every day’s actions allows a
deeper investigation on how persons, computers,
animals, companies etc interact
Networks are everywhere in Nature: from ecology
to the WWW, to food chain, to social networks, to
finance
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 4/39
7. Motivation Multidimensional Spatial analysis Growth analysis
Domain
A complex system is a network of elements that
interacts in a non-linearly way, resulting in an
overall behavior that is difficult to predict.
The digitalization of every day’s actions allows a
deeper investigation on how persons, computers,
animals, companies etc interact
Networks are everywhere in Nature: from ecology
to the WWW, to food chain, to social networks, to
finance
This opened up many interdisciplinary research
areas that are very active
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 4/39
8. Motivation Multidimensional Spatial analysis Growth analysis
History
˝
Started with mathematicians Erdos–Rényi and graph theory
Watts and Strogatz, small world and L , C metrics
Barabási-Albert first introduced the scale-free model, identified hubs and power
law in the degree distribution
Many other works that followed, proposed improvements in the basic statistics and
in the generative models
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 5/39
9. Motivation Multidimensional Spatial analysis Growth analysis
Motivation
The aim of this Thesis was to study Complex Networks (CN) under the most important
dimensions. Key points are the following:
Currently, many studies on CN underestimate the effect of spatial constraints on
the overall evolution
Many models have been proposed in order to create CNs with the same
properties of the observed networks
However, they are not sufficient to describe precisely how networks evolve
That is why other instincts might be at the root of the growth
No methods have been proposed to increase the commitment in users’ communities
For these reasons, we worked on a new framework that is based on these lacking
features. We call it multidimensional.
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 6/39
10. Motivation Multidimensional Spatial analysis Growth analysis
Introduction
So what do we mean by multidimensional?
We mean a novel framework that analyzes complex
networks (CN) along the two fundamental
informative axes:
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 7/39
11. Motivation Multidimensional Spatial analysis Growth analysis
Introduction
So what do we mean by multidimensional?
We mean a novel framework that analyzes complex
networks (CN) along the two fundamental
informative axes:
Space
Possamai Lino Università di Bologna - Università di Padova
Multidimensional analysis of complex networks 7/39
12. Motivation Multidimensional Spatial analysis Growth analysis
Introduction
So what do we mean by multidimensional?
We mean a novel framework that analyzes complex
networks (CN) along the two fundamental
informative axes:
Space
Time
The study of these dimensions was performed by
freezing one axis and simulating the evolution of
the other
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13. Motivation Multidimensional Spatial analysis Growth analysis
Introduction
T HE SPACE DIMENSION
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14. Motivation Multidimensional Spatial analysis Growth analysis
Introduction
Space dimension
The structure of a CN is not 100% completely defined because it
depends on the level of detail with which the system is observed
For instance, biological networks could be analyzed at different
layers. Nodes could be represented as atoms, proteins, cells,
neurons and so on
Until now, no one has considered to study CN as a function of the
detail levels.
Results, properties, features that are valid in a specific level might
not hold in other levels.
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15. Motivation Multidimensional Spatial analysis Growth analysis
Algorithm
Spatial Analysis
So what does it means to view a network at a particular
level?
Let us take a spatial network with information about nodes’
positions over a plane.
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Algorithm
Spatial Analysis
So what does it means to view a network at a particular
level?
Let us take a spatial network with information about nodes’
positions over a plane.
Viewing a network at different precision levels corresponds
to viewing the network at a difference distance from a point
of view.
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17. Motivation Multidimensional Spatial analysis Growth analysis
Algorithm
Spatial Analysis
So what does it means to view a network at a particular
level?
Let us take a spatial network with information about nodes’
positions over a plane.
Viewing a network at different precision levels corresponds
to viewing the network at a difference distance from a point
of view.
This process is modeled utilizing a concept that comes
from the human eyes ability to distinguish two points at
some distance from the observer.
The points are nodes of the network with x, y coordinates
over a plane.
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18. Motivation Multidimensional Spatial analysis Growth analysis
Algorithm
Spatial Analysis
Generally, the telescopic algorithm is a function t : (G × f ) → G′ that takes as
input:
a graph G
fuzziness f (distance)
and produces a resulting graph G′
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19. Motivation Multidimensional Spatial analysis Growth analysis
Algorithm
Spatial Analysis
Generally, the telescopic algorithm is a function t : (G × f ) → G′ that takes as
input:
a graph G
fuzziness f (distance)
and produces a resulting graph G′
In order to emulate the network abstraction capability, we placed a virtual grid on
top of the input graph.
Cell’s dimensions depend on the fuzziness value.
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Algorithm
Spatial Analysis
All the nodes belonging to the same cell are collapsed and
represented by a unique node in the new graph.
If there is an edge from at least one node of the i cell to at
least one of the j cell then the (i, j) edge exists in the new
graph G′ .
With these rules, the long range edges are preserved.
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21. Motivation Multidimensional Spatial analysis Growth analysis
Algorithm
Spatial Analysis
By repeatedly applying this function we create a
fuzziness-varying family of graphs T = {G0 , G1 , . . . Gp }
where p is the number of precision levels.
G0 is the micro view and Gp is the macro view.
This novel analysis then allows creating the telescopic
spectrum of a network, and study, wrt each property of
interest, what changes in the micro-macro shift (in
[Sunbelt2010]).
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22. Motivation Multidimensional Spatial analysis Growth analysis
Datasets
Tracking properties
To characterize the structural properties during the abstraction process, we
consider several features widely used in network literature
Number of nodes, edges, kmax , kmean , standard deviation of k
Physical, topological and metrical diameter
Topological and metrical efficiency:
t 1 1 m 1 1
Eglob = Eglob =
n(n − 1) i=j
hij n(n − 1) i=j
δij
Topological and metrical local efficiency
Topological and metrical costs:
|E| i=j aij lij
Ct = Cm =
n(n − 1)/2 i=j lij
Homophily (degree correlation)
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23. Motivation Multidimensional Spatial analysis Growth analysis
Datasets
Network datasets
Two different classes of networks are considered:
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24. Motivation Multidimensional Spatial analysis Growth analysis
Datasets
Network datasets
Two different classes of networks are considered:
Four subway networks are considered: two from
the U.S., Boston and New York and two from
Europe, Paris and Milan
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25. Motivation Multidimensional Spatial analysis Growth analysis
Datasets
Network datasets
Two different classes of networks are considered:
Four subway networks are considered: two from
the U.S., Boston and New York and two from
Europe, Paris and Milan
The US airline network
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26. Motivation Multidimensional Spatial analysis Growth analysis
Datasets
Network datasets
Two different classes of networks are considered:
Four subway networks are considered: two from
the U.S., Boston and New York and two from
Europe, Paris and Milan
The US airline network
The VirtualTourist online social network (*)
They all are undirected networks.
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27. Motivation Multidimensional Spatial analysis Growth analysis
Results
Global Efficiency
1 1
Topological Eglob
0.8 0.8
Metrical Eglob
0.6 0.6
0.4 Bos 0.4 Bos
NYC NYC
0.2 Par 0.2 Par
Mil Mil
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
We found different results by considering topological and metrical efficiency
Topological: networks with high efficiency at macro level might have low Eglob at
micro
Metrical: stable under detail levels variation.
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28. Motivation Multidimensional Spatial analysis Growth analysis
Results
Global Efficiency
1 1
Topological Eglob
0.8 0.8
Metrical Eglob
0.6 IT 0.6 IT
UK UK
0.4 NL 0.4 NL
AU AU
0.2 IN 0.2 IN
Air Air
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
All the curves start at higher values because of the better structure of SM-SF
networks
Both subways and SM-SF networks will be simpler as f increases, more efficient,
but indistinguishable
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29. Motivation Multidimensional Spatial analysis Growth analysis
Results
Local Efficiency
1 1
Bos Bos
Topological Eloc
0.8 Par 0.8 Par
Metrical Eloc
Mil Mil
0.6 NYC 0.6 NYC
0.4 0.4
0.2 0.2
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
1 1
IT
Topological Eloc
0.8 0.8 UK
Metrical Eloc
NL
0.6 IT 0.6 AU
UK IN
0.4 NL 0.4 Air
AU
0.2 IN 0.2
Air
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
Eloc is stable under our telescopic framework. Low values of local clustering
maintained throughout the spectrum
Results strongly differ from subways. This clearly means that the abstraction
process is able to distinguish the two different principles that guided the evolution
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30. Motivation Multidimensional Spatial analysis Growth analysis
Results
Cost
1 1
Bos Bos
0.8 NYC 0.8 NYC
Par Par
0.6 Mil 0.6 Mil
Cm
Ct
0.4 0.4
0.2 0.2
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
It might be counterintuitive that simple (abstracted) networks are expensive
The cost is directly connected to the efficiency of a network
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31. Motivation Multidimensional Spatial analysis Growth analysis
Results
Cost
1 1
0.8 0.8
0.6 IT 0.6 IT
Cm
Ct
UK UK
0.4 NL 0.4 NL
AU AU
0.2 IN 0.2 IN
Air Air
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
However, when compared to SM-SF networks turn out that the inborn economic
principles that characterize subways are maintained
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32. Motivation Multidimensional Spatial analysis Growth analysis
Results
Randomized fuzziness-varying graphs
In order to understand how the topological and metrical structure of CNs is
affected by the spatial analysis, we used also null models in our simulations
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33. Motivation Multidimensional Spatial analysis Growth analysis
Results
Randomized fuzziness-varying graphs
In order to understand how the topological and metrical structure of CNs is
affected by the spatial analysis, we used also null models in our simulations
In particular, we provided four models that account for different perturbations
+n, shuffling nodes’ positions
+a, rewiring edges
+r, that is the union of +n and +a
+s, scale-free structure (using BA model)
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34. Motivation Multidimensional Spatial analysis Growth analysis
Results
Evolution on randomized networks
1 1
Boston Boston
Topological Eglob
0.8 0.8
Metrical Eglob
0.6 0.6
Norm Norm
0.4 +r 0.4 +r
+a +a
0.2 +n 0.2 +n
+s +s
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
t
In Eglob , randomizations increase the efficiency because they create the right
shortcuts that drop L
Conversely, randomness in a spatial context destroys the global efficiency. Indeed,
when f > 0.3 all the networks will be indistinguishable.
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35. Motivation Multidimensional Spatial analysis Growth analysis
Results
Evolution on randomized networks
1 1
Aus Aus
Topological Eglob
0.8 0.8
Metrical Eglob
0.6 0.6
Norm Norm
0.4 +r 0.4 +r
+a +a
0.2 +n 0.2 +n
+s +s
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Fuzziness Fuzziness
Random perturbations do not alter Eglob because random networks are by
definition very efficient
The destroying effect found in subways is also present but constrained to small
values of f in metrical efficiency
SM-SF are robust because the randomizations do not alter considerably the
networks on the spectrum
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36. Motivation Multidimensional Spatial analysis Growth analysis
Motivation
T HE TIME DIMENSION
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37. Motivation Multidimensional Spatial analysis Growth analysis
Motivation
Time analysis
Many researches in the literature have dealt with proposing generative models
that uncover the key ingredients of network evolution
These are based on simple and advanced local rules that produce a global
behavior that is similar to the steady-state target’s network
Since many of them are based on social systems, we also concentrate on these
types of CNs
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38. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Growth rule I
The random rule assumes that:
Definition
Nodes of the networks randomly connect each other with
uniform probability
pij = k
Empirical tests discovered that real world networks are far from
being random
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39. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Growth rule II
The rule of Preferential attachment assumes that:
Definition
Older nodes are more likely to acquire new links
compared to new ones.
ki
Π(ki ) =
j kj
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40. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Growth rule III
The Social rule assumes that:
Definition
if two people have a friend in common then there is an increased
likelihood that they will become friend in the future
This rule is at the root of the local clustering property (found in
many networks)
Clearly, these rules are not sufficient to completely describe the
evolution of social networks.
There must be some other instincts that trigger the network
evolution
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41. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Settings with special nodes
The contribution of this Thesis is to understand
whether new instincts on top of the previous growth
models can leverage the users’ commitment in
networks
Insight on network evolution with special nodes
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42. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Settings with special nodes
The contribution of this Thesis is to understand mad
whether new instincts on top of the previous growth
models can leverage the users’ commitment in
networks
Insight on network evolution with special nodes
m = number of sirens (6,12)
a = attractiveness
d = activation time span
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43. Motivation Multidimensional Spatial analysis Growth analysis
Growth dynamics
Settings with special nodes
The contribution of this Thesis is to understand mad
whether new instincts on top of the previous growth
models can leverage the users’ commitment in
networks
Insight on network evolution with special nodes
m = number of sirens (6,12)
a = attractiveness
d = activation time span
configurations ci = (m, a, d)
configurations cost Cs = m · a · d
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Simulations
Simulations
Both sequential and simultaneous simulations are considered
The network evolves according to one of the following rules random, aristocratic or
social both at the users and sirens levels
The entire system dynamics is accounted by two almost independent user and
siren subprocesses that evolve according to the previous local rules
In both cases, the future evolution Gt+1 will depend on Gt
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Simulations
Simulations
Sirens are used for a limited time span (d) after that the system will evolve by itself
Sirens acquire new links constantly over time as
es = |V s | · |V | · a
a is the attractiveness of the sirens
q(s)
a(s) = q(u) = 10 ∀u ∈ V s q(u) = 1 ∀u ∈ V
u∈V ∪V s q(u)
In simultaneous simulations, many edges can be created and this number varies
as a function of Eglob
E(Gt−1 )
et = 1 + C · · (nart−1 − 1)
E(Gideal )
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46. Motivation Multidimensional Spatial analysis Growth analysis
Results
Results and Datasets
At this point, based on the framework we provided, we are now able to answer the
following set of fundamental questions:
Are the sirens effective in leveraging users’ commitment in new on line social networks?
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47. Motivation Multidimensional Spatial analysis Growth analysis
Results
Results and Datasets
At this point, based on the framework we provided, we are now able to answer the
following set of fundamental questions:
Are the sirens effective in leveraging users’ commitment in new on line social networks?
What are the best parameters for the same cost configurations?
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48. Motivation Multidimensional Spatial analysis Growth analysis
Results
Results and Datasets
At this point, based on the framework we provided, we are now able to answer the
following set of fundamental questions:
Are the sirens effective in leveraging users’ commitment in new on line social networks?
What are the best parameters for the same cost configurations?
Is the benefit of sirens proportional to the amount of money involved?
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49. Motivation Multidimensional Spatial analysis Growth analysis
Results
Results and Datasets
At this point, based on the framework we provided, we are now able to answer the
following set of fundamental questions:
Are the sirens effective in leveraging users’ commitment in new on line social networks?
What are the best parameters for the same cost configurations?
Is the benefit of sirens proportional to the amount of money involved?
We were particularly interested in on line social networks like VirtualTourist and
Communities
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Results
Q1: Effectiveness
In order to understand whether sirens are effective we compare the simulations
with and without sirens
0.25 0.25
rnd rnd
ari ari
0.2 soc 0.2 soc
0.15 0.15
Eglob
Eglob
0.1 0.1
0.05 CM 0.05 CM + Sir
0 0
0 600 1200 1800 2400 3000 0 20 40 60 80 100 120 140
Step Step
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Results
Q2: Best parameter
What are the best parameters in the siren configurations ci = (m, a, d)?
The configurations that have the higher value of attractiveness are the ones that
perform best
Results are valid for all the rules and networks considered
0.25 aristocratic 0.25 aristocratic
Cs = 1200 Cs = 2400
0.2 0.2
0.15 0.15
Eglob
Eglob
0.1 0.1
0.05 (12,10,10) 0.05 (12,10,20)
(6,10,20) (12,20,10)
(6,20,10) (6,20,20)
0 0
0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70
Step Step
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52. Motivation Multidimensional Spatial analysis Growth analysis
Results
Q3: Benefit
We set the number of sirens and see how the other configuration parameters
influence the growth behavior
We clearly see that the benefit increases, as the cost gets higher. In fact, it is not
proportional to Cs .
0.25 aristocratic 4000
rnd pref
ari pref
0.2 CM+Sir 3000 soc pref
0.15
Eglob
Cs
2000
0.1
(6,10,10) 1000
0.05 (6,10,20)
(6,20,10)
(6,20,20) 0
0 40 60 80 100 120 140
0 40 80 120 160 200 Tmin
Step
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53. Motivation Multidimensional Spatial analysis Growth analysis
Results
Recap of contributions
We introduced a new framework in which we consider the two most important
informative axes along with a CN evolves
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54. Motivation Multidimensional Spatial analysis Growth analysis
Results
Recap of contributions
We introduced a new framework in which we consider the two most important
informative axes along with a CN evolves
The first, spatial analysis, deals with analyzing a network under different detail
levels
Subway networks indexes tend to be more stable under the telescopic variations
Network properties change in the telescopic spectrum: their micro and macro behavior
are different
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55. Motivation Multidimensional Spatial analysis Growth analysis
Results
Recap of contributions
We introduced a new framework in which we consider the two most important
informative axes along with a CN evolves
The first, spatial analysis, deals with analyzing a network under different detail
levels
Subway networks indexes tend to be more stable under the telescopic variations
Network properties change in the telescopic spectrum: their micro and macro behavior
are different
The second, time analysis, models the growth of social networks by using a set of
privileged nodes that promote network evolution
These special nodes are an effective way to increase network efficiency
The benefit increases as cost increases, however it is not proportional
Invest on attractiveness
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Results
Referees reports
From leading expert in the Complex System area
Jesús Gómez Gardeñes (University of Zaragoza)
Overall positive feedback
Acknowledged contributions to state-of-the-art
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Results
Closing remarks and ongoing activities
Consider more spatial networks in order to have a broader coverage and test
whether our findings are still valid
Study force-based network permutations such as Kamada-Kawai and
Fruchterman-Reingold
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58. Motivation Multidimensional Spatial analysis Growth analysis
Results
Closing remarks and ongoing activities
Consider more spatial networks in order to have a broader coverage and test
whether our findings are still valid
Study force-based network permutations such as Kamada-Kawai and
Fruchterman-Reingold
Define network growth that consider mixed rules instead of independent ones
Study the evolution by simultaneously varying the two axes
Continue the work done at Indiana University and in particular verify whether the
idea of “duplex” networked systems can be extended to digital libraries
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59. Motivation Multidimensional Spatial analysis Growth analysis
Results
Thank you
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