Social Interactionseol/SSIIM/1718/seminars/L04 (FR... · 2017. 10. 12. · Topics • The explosion...
Transcript of Social Interactionseol/SSIIM/1718/seminars/L04 (FR... · 2017. 10. 12. · Topics • The explosion...
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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
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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
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• 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
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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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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
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Software Tools
• NetworkX• d3.js• API
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Datasets
● Konect● SNAP● Gephi
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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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