Social recommendation, influence or else
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Presentation at University of Porto Faculty of Enguneering
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Social recommendation, influence or else
- 1. Social recommendation ... … or influence … or else ... Francisco Restivo fjr@fe.up.pt slideshare.net/frestivo
- 2. Topics • The explosion of social interactions • Social Networks • Metrics • Data • Tools • Project ideas 2SSIIM, 2018/10/16
- 3. About me 3SSIIM, 2018/10/16 Lic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. Assoc Signals DSP Multiprocessor systems Biomedical Systems Complex systems Manufacturing systems Multi-agent systems Social systems Lic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. AssocLic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. Assoc
- 5. IBM PC 1981 5SSIIM, 2018/10/16
- 10. Networks • Networks are everywhere • Social, biological, financial, etc • Complex networks • Communities • Contagion • Controversies • Society! 10SSIIM, 2018/10/16
- 12. So easy? • FAGAM • Who decides? • Ethics • Influence • Fake info • Regulation? 12SSIIM, 2018/10/16
- 13. What happened? • Trump • Brexit • Italy • Austria • Brasil • European Parliament 13SSIIM, 2018/10/16
- 14. Social networks • Like, comment, share, cite • Dating • e-Commerce • Payments • Digital marketing • Political marketing • Crime 14SSIIM, 2018/10/16
- 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, 2018/10/16 15
- 16. Is it just Psychology? SSIIM, 2018/10/16 16
- 17. Back in 2015 SSIIM, 2018/10/16 17
- 18. RIPON Voter Engagement Platform SSIIM, 2018/10/16 18
- 21. SO?! SSIIM, 2018/10/16 21 • Let's have a look at graphs and networks
- 23. Euler 1707 - 1783 SSIIM, 2018/10/16 23
- 24. 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, 2018/10/16 24
- 25. 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, 2018/10/16 25
- 26. • Equivalence relations – Reflexive, symmetric, transitive – Equivalence classes • Order relations (partial, total or linear) – reflexive, anti-symmetrical, transitive – Hasse diagrams – x,y xRy yRx (total) SSIIM, 2018/10/16 26 a b x taller than y Be born in the same year Live in the same street Binary relations
- 27. SSIIM, 2018/10/16 27 A OPO LIS MAD PAR LON BER A^2 OPO LIS MAD PAR LON BER A^3 OPO LIS MAD PAR LON BER OPO 0 1 1 1 0 0 OPO 3 0 1 1 2 2 OPO 2 7 6 6 1 1 LIS 1 0 0 0 1 1 LIS 0 3 2 2 0 0 LIS 7 0 2 2 5 5 MAD 1 0 0 1 0 1 MAD 1 2 3 1 1 0 MAD 6 2 2 5 3 5 PAR 1 0 1 0 1 0 PAR 1 2 1 3 0 1 PAR 6 2 5 2 5 3 LON 0 1 0 1 0 0 LON 2 0 1 0 2 1 LON 1 5 3 5 0 1 BER 0 1 1 0 0 0 BER 2 0 0 1 1 2 BER 1 5 5 3 1 0 opo lis mad par lon par parber Composition of relations
- 28. • Usually not transitive (a likes b and b likes c but ...) • “Equivalence” relations – No equivalence classes – But communities, clusters, etc • “Order” relations (partial, total) – No Hasse diagrams – Rankings, proeminence indexes, etc SSIIM, 2018/10/16 28 Real life relations
- 29. Global metrics • Number of vertexes 5 • Number of edges 11 • Number of components 1 • Diameter 2 • Density 0.55 SSIIM, 2018/10/16 29
- 30. 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 30SSIIM, 2018/10/16
- 31. Champions League all finals SSIIM, 2018/10/16 31 Eigenvector centrality Wins Milan 6 Real Madrid 10 Ajax 4 Liverpool 5 Internazionale 3 Juventus 2 Benfica 2 Marseille 1 Barcelona 4 Manchester United 3 Celtic 1 Hamburg 1 Borussia Dortmund 1 PSV Eindhoven 1 Red Star Belgrade 1 Steaua Bucuresti 1 Bayern Munich 5 Feyenoord 1 Nottingham Forest 2 Porto 2 Aston Villa 1 Chelsea 1
- 32. Common Tasks • Measuring “importance” – Centrality, prestige, influence (incoming links) • Diffusion modeling – Epidemiological • Clustering – Blockmodeling, Girvan-Newman, Chinese whisper • Visualization/Privacy/etc. 32SSIIM, 2018/10/16
- 33. Community detection • Network data is related to graph properties • Overlapping communities are sometimes allowed • Real world data is big SSIIM, 2018/10/16 33
- 34. Modularity • Compares number of edges with number of edges of a random network • Maximize Q is NP-hard SSIIM, 2018/10/16 34 jC,iCδ ij ijPijA m2 1 Q m2 jkik ijP
- 35. Queen @ Spotify in Gephi SSIIM, 2018/10/16 35
- 36. Dynamics • Networks may have a temporal dimension • Interactions – follow, like, share, mention, retweet, hashtag, etc – occur in sequence • Network properties evolve in time SSIIM, 2018/10/16 36
- 38. Reports, labs, tools, datasets • A quick tour on this rapidly evolving field SSIIM, 2018/10/16 38
- 47. Digital Methods Initiative SSIIM, 2018/10/16 47
- 48. Queen @ Spotify SSIIM, 2018/10/16 48
- 49. Eurovision song contest SSIIM, 2018/10/16 49
- 50. Ideas? ● Find and use APIs ● Crawl Instagram ● Hashtags co-occurrences (Twitter, Tumblr) ● Detect fake/abnormal behaviours ● Use your imagination! SSIIM, 2018/10/16 50
- 51. Thank you! SSIIM, 2018/10/16 51