Social interactions
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Multi-agent systems and beyond Presentation at FEUP 2016 October 6
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Social interactions
- 1. Social Interactions Multi-agent systems and beyond Francisco Restivo fjr@fe.up.pt slideshare.net/frestivo
- 2. Topics • The explosion of social interactions • ICT and social interactions • Social Networks • Metrics • Data • Tools • Project ideas 2SSIIM, 2016/10/06
- 6. How teens communicate SSIIM, 2016/10/06 6
- 7. Where are we now? 7SSIIM, 2016/10/06
- 8. Social networks Social interactions • Like, comment, share • e-Commerce • Payments • Digital marketing • Political marketing • etc 8SSIIM, 2016/10/06
- 10. Networks • Networks are everywhere • Social, biological, financial, etc • Complex networks • Communities reveal properties of networks • Contagion • Controversies • Society! 10SSIIM, 2016/10/06
- 15. Where are we? ● Complex networks ● Actors influencing and being influenced by other actors ● But humans are not software agents ● Difficult to establish consensus ● Intelligence highly needed ● Maybe biology could inspire us... SSIIM, 2016/10/06 15
- 16. SO?! SSIIM, 2016/10/06 16 • Let's have a look at graphs and networks
- 17. Euler 1707 - 1783 SSIIM, 2016/10/06 17
- 18. Basics of graphs and networks • G = (V, E) • O(G) = |V| order • S(G) = |E| size • A adjacency matrix • Ki degree of vertex i • Directed/undirected SSIIM, 2016/10/06 18
- 19. 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, 2016/10/06 19
- 20. Global metrics • Number of vertexes 5 • Number of edges 11 • Number of components 1 • Diameter 2 • Density 0.55 SSIIM, 2016/10/06 20
- 21. Centrality Measures • Degree centrality – 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 • Bibliometric + Internet style (quality of edges) – PageRank, eigenvector 21SSIIM, 2016/10/06
- 22. Champions league Pagerank SSIIM, 2016/10/06 22
- 23. Common Tasks • Measuring “importance” – Centrality, prestige (incoming links) • Diffusion modeling – Epidemiological • Clustering – Blockmodeling, Girvan-Newman, Chinese whisper • Visualization/Privacy/etc. 23SSIIM, 2016/10/06
- 24. A simple study • The universe of students that opted for a FEUP course in 2016 (1st call) – http://www.dges.mctes.pt/coloc/2016/col1listas.asp • The choices of each student (inside FEUP) • Each student connected to all courses selected • Force layout SSIIM, 2016/10/06 24
- 26. Community detection • Communities and clusters are different • Network data is related to graph properties • Real world data is big SSIIM, 2016/10/06 26
- 27. Modularity • Compares number of edges with number of edges of a random network • Maximize Q is NP-hard SSIIM, 2016/10/06 27 ( ) ( )jg,ig ij ijPijA m2 1 Q m2 jkik ijP δ∑ −= =
- 28. Community detection algorithms • Clauset-Newman-Moore • Wakita-Tsurumi • Girvan-Newman • Chinese whispers • Link communities • etc SSIIM, 2016/10/06 28
- 29. Dynamics • Networks have a temporal dimension • Interactions – follow, like, share, mention, retweet, hashtag, etc – occur in sequence • Network properties evolve in time SSIIM, 2016/10/06 29
- 30. 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, 2016/10/06 30
- 31. NOW? • What's people really doing? SSIIM, 2016/10/06 31
- 40. Ideas? ● Crawl Instagram ● Hashtags co-occurrences (Twitter, Instagram) ● Explore a large dataset (Offshore Leaks) ● Detect abnormal behaviours ● Use your imagination! SSIIM, 2016/10/06 40
- 41. Thank you! SSIIM, 2016/10/06 41
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