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?
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
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
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
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
Community detection from a computational social science perspectiveDavide Bennato
This is the talk I gave at the Lipari Summer School on Computational Social Science, 2014. Which are the sociological strategies to detect communities in social media? How we can define a community form a computational social science point of view?
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Paolo Rosso "On irony detection in social media"AINL Conferences
Каковы лингвистические паттерны, которым следуют пользователи социальных сетей, чтобы высказывать иронию в совсем коротких фразах? Лингвистические средства - такие как неоднозначность, непоследовательность, неожиданность эмоциональный контекст, гораздо более широкий, чем просто негативная или позитивная тональность - играют очень важную роль триггеров иронии. В иронических текстах буквальный смысл сообщения как правило отрицается, но формальные маркеры отрицания отсутствуют. Это делает задачу определения иронии очень сложной. В своем выступлении я опишу как ирония выражается в социальных сетях (Twitter, Amazon, Facebook и др.) и каково современное положение дел в автоматическом определении иронии. Определение иронии очень важно для таких задач анализа текста как определение тональности сообщения, извлечение мнений, или анализ репутаций, и существует определенный интерес исследовательского сообщества к этой теме. На конференции SemEval 2015 будет организована задача-соревнование по определению тональности фигуративного языка в Твиттере (Sentiment Analysis of Figurative Language in Twitter, http://alt.qcri.org/semeval2015/task11/). В конце я коснусь еще более сложной проблемы различения иронии, сатиры и сарказма, например: Если вам тяжело смеяться над собой, я буду счастлив сделать это за вас.
This isn't what I thought it was: community in the network ageNancy Wright White
A narrated version can be found here: https://www.youtube.com/watch?v=YB82kbj-NXw This was a short remote presentation that was part of a panel at the CACUSS 12.0: Engaging Digital Citizens conference <http: /> in Vancouver BC, Canada.
The Network, the Community and the Self-CreativityVince Cammarata
Lulu.com is a marketplace where “authors” - individuals, companies
and groups - can publish and sell a variety of digital content including
books, music, video, software, calendars, photos and artwork...
UNIT 1: INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
PRESENTATION:
Staying Afloat in the Blue Ocean Waters of Social Media
WHO/WHERE/WHEN:
Custom Publishing Conference
Ft. Lauderdale, FL – March 23, 2009
DESCRIPTION
The Social Media waters can be treacherous. Many companies have capsized trying to capitalize on participating in this online customer-driven channel. Learn as John Moore, respected marketer and blogger, shares strategic advice on how any business can successfully navigate the Social Media waters by embracing and enlivening the consumer-driven online conversation.
PRESENTER:
John Moore, marketingologist
Brand Autopsy Marketing Practice
www.BrandAutopsy.com
LSS'11: Charting Collections Of Connections In Social MediaLocal Social Summit
Keynote Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
Slides for talk at ConTech 2011 the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.
Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping)a sets of nodes such that each set of nodes is densely connected internally.
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
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.
Similar to Group and Community Detection in Social Networks (20)
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Group and Community Detection in Social Networks
1.
2. Outline
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?
4. Definitions
Social Network:
finite set or sets of actors and the relation or relations defined on them.
Actor = Node = Point = Agent:
social entities such as persons, organizations, cities, etc.
Tie = Link = Edge = Line = Arc:
represents relationships among actors.
Relation:
collection of ties of a specific kind among members of a group.
5. Attributes of Actor (nodes)
- People can be queried
about different
features, like
( age, gender, race,
socioeconomic status,
place of residence,
grade in school, etc. )
7. Definition : Social Groups and Communities
– “Two or more people , who interact with one another,
share similar characteristics and attributes and
collectively have a sense of unity”
– Actors who have all possible ties among themselves
The real-world problem is:
“Finding Groups And Communities In Social Networks”
8. • Social networks and the social network analysis:
– Is an interdisciplinary academic field
(social psychology, sociology, statistics, and graph theory)
– 1930; first sociograms in the to study interpersonal relationships, by
Jacob
– 1950; sociograms approaches mathematically formalized
– 1980; theories and methods of
social networks became popular
in the social and behavioral sciences
– Social network analysis is now one
of the major paradigms in
contemporary sociology
http://www.cmu.edu/joss/content/articles/volume1/Freeman.html
9. Why to find social groups and communities?
–behavior analysis
–location-based interaction analysis
–recommender systems development
–link prediction
–customer interaction and analysis & marketing
–media use
–Security
–Social studies
10. 3. How to Construct Graph From
Real-world Problem?
11. - Shared Attributes: Actors are grouped based on the shared
attributes among them. i.e. Group of four people (Bob, Carol, Ted,
and Alice)
- Blue for males, red for females
http://faculty.ucr.edu/~hanneman/nettext/C3_Graphs.html
Bob Carol
TedAlice
12. - Attribute 1: "close friends”: who they regarded as close friends in
the group?
A directed graph of friendship ties
Bob Carol
TedAlice
Bob, Carol, and Ted form a "clique" (i.e. each is connected to each of the others)
Alice is a "pendant" (tied to the group by only one connection)
13. - Attribute 2: “Spouse”
A directed graph of spousal ties
Bob Carol
TedAlice
14. 4. What Graph Theory Problem Getting
From Real-world Problem?
15. • Clique problem: refers to any problem to find
particular (complete) subgraphs ("cliques") in
a graph,
• i.e., sets of elements where each pair of
elements is connected.
http://sebastian.doc.gold.ac.uk/
16. • Note: the notion of clique here
dose not necessary refers to a
complete subgraph,
http://sebastian.doc.gold.ac.uk/
Complete Graph: there's an edge between any two node
Dense Graph: number of edges is close to the maximal number of edges
Sparse Graph: when it has only a few edges
17. Dense Graph Definition
• A graph G = (V, E) is said to be dense if for every v ∈ V ,
degree(v) > n/2, where n = |V|
• Density is the ratio between the number of edges |E|
and the number of vertices |V|.
• Density for undirected graphs:
• The maximal density is 1 = complete graphs
• Maximum number of edges ½ |V| (|V|−1)
http://www.cc.gatech.edu/~vigoda/MCMC_Course/Lec7.pdf
18. Complexity of the problem
• Clique problem is NP-Complete problem
– k-clique problem, the input is an undirected graph
and a number k, and the output is a clique of size
k if one exists (or, sometimes, all cliques of size k)
20. Small-World Graph = Scale-Free Graph
– most nodes are not neighbors of one another, but most
nodes can be reached from every other by a small number
of hops or steps.
– Specifically, a small-world network is defined to be a
network where the typical distance L between two
randomly chosen nodes (the number of steps required)
grows proportionally to the logarithm of the number of
nodes N in the network, that is:
http://www.lenddo.com/blog/2012/06/facebook-proves-it%E2%80%99s-a-small-world-after-all-we-are-all-connected-by-six-degrees-or-less/
Last time by: Reem
22. • Community Structure: Real-world social graphs are found to exhibit a
modular structure; with nodes forming groups, and possibly groups within
groups
– In a modular graph, the nodes form communities where groups of nodes in
the same community are tighter connected to each other than to those nodes
outside the community
• Heavy-tailed Degree Distribution:
– few “hubs”,
– most nodes have few neighbors
- The degree distribution has a power law (functional relationship)
- many low degree nodes - only a few high degree nodes in real graphs
• Small Diameter: also known as the ‘small-world phenomenon’ or the ‘six
degrees of separation’
M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69:026113, 2004.
23. 7. How to Find Communities nnd Groups
in Social Networks? (Algorithms)
24. Taxonomy of Community Criteria
- Community detection methods categories:
• Node-Centric Community Detection
– Each node in a group satisfies certain properties
• Group-Centric Community Detection
– Consider the connections within a group as a whole. The group has
to satisfy certain properties without zooming into node-level
• Network-Centric Community Detection
– Partition the whole network into several disjoint sets
• Hierarchy-Centric Community Detection
– Construct a hierarchical structure of communities
27. Clique Percolation Method (CPM)
• Clique is a very strict definition, unstable
• Normally use cliques as a core to find larger communities
• CPM is such a method to find overlapping communities
– Input
• A parameter k, and a network
– Procedure
1. Find out all cliques of size k in a given network
2. Construct a clique graph. Two cliques are adjacent if
they share k-1 nodes
3. Each connected components in the clique graph
form a community 27
31. 8. How to Interpret Graph Solution
Back to Real-life Problem?
32. - Finding Cliques in the Social Graph of the Social
Network leads to the communities and groups
inside the Social Networks, based on the
attributes and characteristics of actors in the
communities
33. References
• Community detection in Social Media, (2012), Symeon Papadopoulos, Yiannis Kompatsiaris,
Athena Vakali, Ploutarchos Spyridonos, Data Mining and Knowledge Discovery May 2012,
Volume 24, Issue 3, pp 515-554
• Community Detection in Graphs, (2010), Santo Fortunato, Complex Networks and Systems
Lagrange Laboratory, ISI Foundation, Viale S. Severo 65, 10133, Torino,I-ITALY.
• A Comparison of Community Detection Algorithms on Artificial Networks, (2009), Günce
Keziban Orman1,2 and Vincent Labatut , Discovery Science Lecture Notes in Computer
Science Volume 5808, pp 242-256
• Social Network Analysis. Methods and Applications, (2008), Wasserman, Stanley, Faust,
Katherine, Cambridge, University Press
• Computing Communities in Large Networks Using Random Walks, (2005), Pascal Pons and
Matthieu Latapy, Computer and Information Sciences – ISCIS, Lecture Notes in Computer
Science Volume 3733, 2005, pp 284-293
• Introduction to social network methods, (2005) Robert A. Hanneman and Mark Riddle,
University of California,