Social Interactions ...… and beyond

Francisco Restivofjr@fe.up.pt

slideshare.net/frestivo

Topics

• The explosion of social interactions• ICT and social interactions• Social Networks • Metrics• Data• Tools • Project ideas

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Where are we now?

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Social media usage 2016

How teens communicate

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Networks

• Networks are everywhere• Social, biological, financial, etc• Complex networks• Communities reveal properties of networks• Contagion• Controversies• Society!

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Social networks

Social interactions• Like, comment, share, cite • e-Commerce• Payments• Digital marketing• Political marketing• etc

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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...

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SO?!

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• Let's have a look at graphs and networks

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Euler 1707 - 1783

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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

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Representation of networks

• Matrixes, graphs, edge lists, etc

A B C D EA 0 1 1 1 0B 1 0 1 0 1C 0 0 0 1 0D 0 1 1 0 0E 1 1 0 0 0

A BA CA DB AB CB EC DD BD CE AE B

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• Equivalence relations– Reflexive, symmetric, transitive– Equivalence classes

• Order relations (partial, total or linear)– reflexive, anti-symmetrical, transitive– Hasse diagrams– x,y xRy yRx (total)

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a bx taller than y

Be born in the same yearLive 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 BEROPO 0 1 1 1 0 0 OPO 3 0 1 1 2 2 OPO 2 7 6 6 1 1LIS 1 0 0 0 1 1 LIS 0 3 2 2 0 0 LIS 7 0 2 2 5 5MAD 1 0 0 1 0 1 MAD 1 2 3 1 1 0 MAD 6 2 2 5 3 5PAR 1 0 1 0 1 0 PAR 1 2 1 3 0 1 PAR 6 2 5 2 5 3LON 0 1 0 1 0 0 LON 2 0 1 0 2 1 LON 1 5 3 5 0 1BER 0 1 1 0 0 0 BER 2 0 0 1 1 2 BER 1 5 5 3 1 0

opo

lis

mad

parlon

par

parber

Composition of relations

• 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

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Real life relations

Global metrics

• Number of vertexes 5

• Number of edges 11

• Number of components 1

• Diameter 2

• Density 0.55

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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

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Champions league Pagerank

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Common Tasks

• Measuring “importance”– Centrality, prestige, influence (incoming links)

• Diffusion modeling– Epidemiological

• Clustering– Blockmodeling, Girvan-Newman, Chinese whisper

• Visualization/Privacy/etc.

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Community detection

• Communities and clusters are different • Network data is related to graph properties• Real world data is big

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Modularity

• Compares number of edges with number of edges of a random network

• Maximize Q is NP-hard

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jC,iCδij

ijPijAm21

Q

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ijP

Dynamics

• Networks have a temporal dimension• Interactions – follow, like, share, mention,

retweet, hashtag, etc – occur in sequence• Network properties evolve in time

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• Gephi• SocNetV• Netlogo

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Tools

Software Tools

• NetworkX• d3.js• API

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Datasets

● Konect● SNAP● Gephi

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Digital Methods Initiative

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Queen @ Spotify

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Queen @ Spotify in Gephi

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#barcelona @ Tumblr in Gephi

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Ideas?

● Find and use APIs● Crawl Instagram● Hashtags co-occurrences (Twitter, Tumblr)● Detect fake/abnormal behaviours● Use your imagination!

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Thank you!

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