Social interactions
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This document discusses social interactions and networks. It covers topics like the growth of social networks, how people communicate, complex network analysis using metrics like centrality and modularity, dynamics and temporal aspects of networks, and the impact of bots. Tools for network analysis including Gephi, NetworkX and datasets from sources like Konect and SNAP are also mentioned. The document provides an overview of social network analysis and suggests possible project ideas involving crawling social media data and exploring large datasets.
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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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