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![Chinese whispers [Biemann]
• a
Randomized graph-clustering algorithm, which is time-linear in the
number of edges.
It can be viewed as a simulation of an agent-based social network.
SSIIM, 2015/09/22 33](https://image.slidesharecdn.com/communitiesdynamics-150922030609-lva1-app6892/75/Communities-and-dynamics-in-social-networks-33-2048.jpg)
![Link communities [Ahn et al]
Communities in networks often overlapsuch that nodes
simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical
organization, where communities are recursively grouped into a
hierarchical structure.
SSIIM, 2015/09/22 34](https://image.slidesharecdn.com/communitiesdynamics-150922030609-lva1-app6892/75/Communities-and-dynamics-in-social-networks-34-2048.jpg)
Uploaded byFrancisco Restivo
Communities and dynamics in social networks
The document discusses communities and dynamics in social networks. It covers topics such as graph theory, metrics for analyzing networks, algorithms for detecting communities, and how networks change over time. Various network analysis tools and datasets are also mentioned. The presentation aims to provide an overview of social network analysis, including techniques for modeling network structure and dynamics.
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Communities and dynamics in social networks
- 1. Communities and Dynamics inSocial Networks Francisco Restivo [email protected] slideshare.net/frestivo
- 2. Topics • Graphs andsocial networks • Some metrics • Communities • Dynamics • Fraud • Software • etc 2SSIIM, 2015/09/22 Zachary karate club
- 3. Networks • Networks areeverywhere • Social, biological, financial, etc • Complex networks • Communities reveal properties of networks • Contagion • Controversies 3SSIIM, 2015/09/22
- 4. Euler 1707 -1783 SSIIM, 2015/09/22 4
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- 9. Social networks • Internetchanged everything • Social interactions • Sharing • e-Commerce • Payments • Digital marketing • Political marketing • etc 9SSIIM, 2015/09/22
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- 12. How teens communicate SSIIM,2015/09/22 12
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- 16. Basics of graphsand networks • G = (V, E) • O(G) = |V| order • S(G) = |E| size • A adjacency matrix • Ki degree of vertex I • Directed/undirected SSIIM, 2015/09/22 16
- 17. Representation of networks •Matrixes, graphs, edge lists, etc A B C D E A 0 1 1 1 0 B 1 0 1 0 1 C 0 0 0 1 0 D 0 1 1 0 0 E 1 1 0 0 0 A B A C A D B A B C B E C D D B D C E A E B SSIIM, 2015/09/22 17
- 18. Global metrics • Numberof vertexes 5 • Number of edges 11 • Number of components 1 • Diameter 2 • Density 0.55 SSIIM, 2015/09/22 18
- 19. Common Tasks • Measuring“importance” – Centrality, prestige (incoming links) • Link prediction • Diffusion modeling – Epidemiological • Clustering – Blockmodeling, Girvan-Newman, Chinese whisper • Structure analysis – Motifs, Isomorphisms, etc. • Visualization/Privacy/etc. from Eytan Adar slides 19SSIIM, 2015/09/22
- 20. Centrality Measures • Degreecentrality – Edges per node (the more, the more important the node) • Closeness centrality – How close the node is to every other node • Betweenness centrality – How many shortest paths go through the edge node (communication metaphor) • Information centrality – All paths to other nodes weighted by path length • Bibliometric + Internet style – PageRank from Eytan Adar slides 20SSIIM, 2015/09/22
- 21. Champions league Pagerank SSIIM,2015/09/22 21
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- 24. Community detection • Communitiesand clusters are different • Network data is related to graph properties • Real world data is big SSIIM, 2015/09/22 24
- 25. Community detection algorithms •Clauset-Newman-Moore • Wakita-Tsurumi • Girvan-Newman • Chinese whispers • Link communities • etc SSIIM, 2015/09/22 25
- 26. Modularity • Compares numberof edges with number of edges of a random network • Maximize Q is NP-hard SSIIM, 2015/09/22 26 j g, i g ij ij P ij A m2 1 Q m2 j k i k ij P
- 27. Clauset-Newman-Moore A hierarchical agglomerationalgorithm for detecting community structure which is faster than many competing algorithms. Its running time on a network with n vertices and m edges is O(md log n) where d is the depth of the dendrogram describing the community structure. SSIIM, 2015/09/22 27
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- 29. Wakita-Tsurumi CNM algorithm doesnot scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. A simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. SSIIM, 2015/09/22 29
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- 31. Girvan-Newman A property thatis found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. SSIIM, 2015/09/22 31
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- 33. Chinese whispers [Biemann] •a Randomized graph-clustering algorithm, which is time-linear in the number of edges. It can be viewed as a simulation of an agent-based social network. SSIIM, 2015/09/22 33
- 34. Link communities [Ahnet al] Communities in networks often overlapsuch that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. SSIIM, 2015/09/22 34
- 35. Dynamics • Networks havea temporal dimension • Interactions – follow, like, share, mention, retweet, hashtag, etc – occur in sequence • Network properties evolve in time SSIIM, 2015/09/22 35
- 36. Impact of bots •The use of bots is increasing • In Twitter, one in 20 active accounts are fake • In Facebook, one in 100 active accounts is estimated to be fake • Better auditing algorithms are needed SSIIM, 2015/09/22 36
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- 40. Software Tools • NodeXL •Gephi • NetworkX • Meerkat • netvizz • d3.js • API SSIIM, 2015/09/22 40
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- 45. Datasets I keep mycollection here https://sites.google.com/site/frestivo/networked-life/databases There is another in Quora Where can I find large datasets open to the public? My collection of papers: http://tinyurl.com/qzjp6rg SSIIM, 2015/09/22 45
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