Presentation at FEUP, in 2017-10-12

1. Social Interactions ...
… and beyond
Francisco Restivo
fjr@fe.up.pt
slideshare.net/frestivo

2. Topics
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The explosion of social interactions
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ICT and social interactions
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Social Networks
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Metrics
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Data
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Tools
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Project ideas
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7. Where are we now?
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Social media usage 2016

10. How teens communicate
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12. 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 reveal properties of networks
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Contagion
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Controversies
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Society!
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15. Social networks
Social interactions
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Like, comment, share, cite
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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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etc
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16. 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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17. SO?!
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•
Let's have a look at graphs and networks

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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. Representation 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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Hasse diagram
m divides n

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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

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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 (the more, the more important the 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. Champions league Pagerank
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29. 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
– Blockmodeling, Girvan-Newman, Chinese whisper
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Visualization/Privacy/etc.
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30. Community detection
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Communities and clusters are different
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Network data is related to graph properties
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Real world data is big
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31. Modularity
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Compares number of edges with number of
edges of a random network
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Maximize Q is NP-hard
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   jC,iCδ
ij
ijPijA
m2
1
Q
m2
jkik
ijP
 


32. Dynamics
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Networks 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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40. •
Gephi
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SocNetV
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Netlogo
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Tools

41. Software Tools
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NetworkX
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d3.js
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API
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42. Datasets
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Konect
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SNAP
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Gephi
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43. Digital Methods Initiative
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44. Queen @ Spotify
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45. Queen @ Spotify in Gephi
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46. #barcelona @ Tumblr in Gephi
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47. Ideas?
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Find and use APIs
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Crawl Instagram
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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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48. Thank you!
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