Social Interactions
Multi-agent systems and beyond
Francisco [email protected]/frestivo
Topics
The explosion of social interactions
ICT and social interactions
Social Networks
Metrics
Data
Tools
Project ideas
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Network growth
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How teens communicate
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Where are we now?
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Social networks
Social interactionsLike, comment, share
e-Commerce
Payments
Digital marketing
Political marketing
etc
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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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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
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
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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 centralityHow close the node is to every other
node
Betweenness centralityHow 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 (incoming links)
Diffusion modelingEpidemiological
ClusteringBlockmodeling, Girvan-Newman, Chinese whisper
Visualization/Privacy/etc.
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A simple study
The universe of students that opted for a FEUP course in 2016
(1st call)http://www.dges.mctes.pt/coloc/2016/col1listas.asp
The choices of each student (inside FEUP)
Each student connected to all courses selected
Force layout
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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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Community detection algorithms
Clauset-Newman-Moore
Wakita-Tsurumi
Girvan-Newman
Chinese whispers
Link communities
etc
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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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Impact of bots
The use of bots is increasing
In Twitter, one in 20 active accounts are fake
In Facebook, one in 100 active accounts is estimated to be
fake
Better auditing algorithms are needed
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NOW?
What's people really doing?
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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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Ideas?
Crawl Instagram
Hashtags co-occurrences (Twitter, Instagram)
Explore a large dataset (Offshore Leaks)
Detect abnormal behaviours
Use your imagination!
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Thank you!
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