FEUP, SSIIM, 2019-10-07

1. Visualizing... networks
Francisco Restivo
fjr@fe.up.pt
slideshare.net/frestivo

2. Topics
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The explosion of Big Data
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Data
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Social Networks
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Technologies
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Tools
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Projects
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3. About me
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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

4. Internet timeline
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5. Apollo 11 1969
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6. Porto 1970
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7. IBM PC 1981
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10. Networks
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Networks are everywhere
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Social, biological, financial, etc
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Complex networks
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Communities
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Contagion
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Controversies
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Society!
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11. Society!
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Social
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Education
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Industry
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Economy
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Influence
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Politics
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Digital transformation
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12. Social networks
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Like, comment, share, cite
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Dating
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e-Commerce
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Payments
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Digital marketing
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Political marketing
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Crime
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13. Where are we?
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Complex networks
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Actors influencing and being influenced
by other actors
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But humans are not software agents
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Difficult to establish consensus
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Intelligence highly needed
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Maybe biology could inspire us...
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14. Artificial intelligence
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Artificial Intelligence
MAS + ML + NN + DL + etc
Data + networks
People

15. RIPON Voter Engagement Platform
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16. Recommender systems
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17. Influencers
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18. SO?!
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Let's have a look at networks and graphs

19. Euler 1707 - 1783
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20. Basics of graphs and networks
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G = (V, E)
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O(G) = |V| order
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S(G) = |E| size
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A adjacency matrix
• Ki
degree of vertex i
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Directed/undirected
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21. Representations of networks
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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
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22. •
Equivalence relations
– Reflexive, symmetric, transitive
– Equivalence classes
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Order relations (partial, total or linear)
– reflexive, anti-symmetrical, transitive
– Hasse diagrams
– ∀x,y xRy ∨ yRx (total)
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a ≤ b
x taller than y
Be born in the same year
Live in the same street
Binary relations

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Composition of relations
A Juventus 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
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Eigenvalues and eigenvectors

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Usually not transitive (a likes b and b likes c
but ...)
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“Equivalence” relations
– No equivalence classes
– But communities, clusters, etc
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“Order” relations (partial, total)
– No Hasse diagrams
– Rankings, proeminence indexes, etc
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Real life relations

26. Global metrics
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Number of vertexes 5
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Number of edges 11
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Number of components 1
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Diameter 2
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Density 0.55
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27. Centrality Measures
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Degree centrality
– Edges per node
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Closeness centrality
– How close the node is to every other node
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Betweenness centrality
– How many shortest paths go through the edge node
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Bibliometric + Internet style (quality of edges)
– PageRank, eigenvector
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28. Common Tasks
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Measuring “importance”
– Centrality, prestige, influence (incoming links)
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Diffusion modeling
– Epidemiological
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Clustering
– Leiden, Girvan-Newman, Chinese whisper
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Visualization/Privacy/etc.
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29. Dynamics
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Networks may have a temporal dimension
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Interactions – follow, like, share,
mention, retweet, hashtag, etc – occur in
sequence
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Network properties evolve in time
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30. People
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34. Books
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36. Tools
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41. Datasets
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polsys.net

44. Ideas?
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Spectral!

48. Computational Thinking
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Formulating problems in a way that a
programmable machine can solve them
efficiently
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Understanding the way programmable
machines operate
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Understanding of the role computation
can play in solving problems
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49. Ideas?
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Find and use APIs
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Graph databases | Neo4j | GQL
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Visualize citation networks
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Hashtags co-occurrences (Twitter,
Tumblr)
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Detect fake/abnormal behaviours
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Use your imagination!
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50. Thank you!
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