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Visualizing networks
The document discusses visualizing networks and discusses topics like social networks, technologies for analyzing networks, and computational tools for working with network data. It provides an overview of network analysis, including common metrics like centrality and techniques like clustering. Examples of network analysis tasks and real-world networks are described.
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Visualizing networks
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- 2. Topics • The explosion ofBig Data • Data • Social Networks • Technologies • Tools • Projects 2SSIIM, 2019/10/07
- 3. About me 3SSIIM, 2019/10/07 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 Networks Lic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. AssocLic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. Assoc
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- 5. Apollo 11 1969 5SSIIM,2019/10/07
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- 10. Networks • Networks are everywhere • Social,biological, financial, etc • Complex networks • Communities • Contagion • Controversies • Society! 10SSIIM, 2019/10/07
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- 12. Social networks • Like, comment,share, cite • Dating • e-Commerce • Payments • Digital marketing • Political marketing • Crime 12SSIIM, 2019/10/07
- 13. Where are we? ● Complexnetworks ● 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, 2019/10/07 13
- 14. Artificial intelligence SSIIM, 2019/10/0714 Artificial Intelligence MAS + ML + NN + DL + etc Data + networks People
- 15. RIPON Voter EngagementPlatform SSIIM, 2019/10/07 15
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- 18. SO?! SSIIM, 2019/10/07 18 • Let'shave a look at networks and graphs
- 19. Euler 1707 -1783 SSIIM, 2019/10/07 19
- 20. 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, 2019/10/07 20
- 21. Representations 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, 2019/10/07 21
- 22. • 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, 2019/10/07 22 a ≤ b x taller than y Be born in the same year Live in the same street Binary relations
- 23. 23 Composition of relations AJuventus Wolves City PSV Barcelona Ronaldo 1 0 0 0 0 Moutinho 0 1 0 0 0 0 0 1 0 0 Bernardo 0 0 1 0 0 Bruma 0 0 0 1 0 Rúben 0 1 0 0 0 0 0 0 0 1 (jogador, clube) Cancelo Semedo x B Espanha Juventus 1 0 0 0 Wolves 0 1 0 0 City 0 1 0 0 PSV 0 0 1 0 Barcelona 0 0 0 1 (clube, país) Itália Inglaterra Holanda = AB Espanha Ronaldo 1 0 0 0 Moutinho 0 1 0 0 0 1 0 0 Bernardo 0 1 0 0 Bruma 0 0 1 0 Rúben 0 1 0 0 0 0 0 1 (jogador, país) Itália Inglaterra Holanda Cancelo Semedo SSIIM, 2019/10/07
- 24. SSIIM, 2019/10/07 24 Eigenvaluesand eigenvectors
- 25. • 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, 2019/10/07 25 Real life relations
- 26. Global metrics • Number ofvertexes 5 • Number of edges 11 • Number of components 1 • Diameter 2 • Density 0.55 SSIIM, 2019/10/07 26
- 27. Centrality Measures • Degree centrality –Edges per 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 27SSIIM, 2019/10/07
- 28. Common Tasks • Measuring “importance” –Centrality, prestige, influence (incoming links) • Diffusion modeling – Epidemiological • Clustering – Leiden, Girvan-Newman, Chinese whisper • Visualization/Privacy/etc. 28SSIIM, 2019/10/07
- 29. Dynamics • Networks may havea temporal dimension • Interactions – follow, like, share, mention, retweet, hashtag, etc – occur in sequence • Network properties evolve in time SSIIM, 2019/10/07 29
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- 48. Computational Thinking ● Formulating problemsin a way that a programmable machine can solve them efficiently ● Understanding the way programmable machines operate ● Understanding of the role computation can play in solving problems SSIIM, 2019/10/07 48
- 49. Ideas? ● Find and useAPIs ● Graph databases | Neo4j | GQL ● Visualize citation networks ● Hashtags co-occurrences (Twitter, Tumblr) ● Detect fake/abnormal behaviours ● Use your imagination! SSIIM, 2019/10/07 49
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