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Visualising and analysing
networks
Francisco Restivo
fjr@fe.up.pt
slideshare.net/frestivo
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. Assoc
Lic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. Assoc.
1970
IBM PC 1981
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Internet timeline
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Topics
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The explosion of Big Data
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Networks everywhere
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Social Networks
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Technologies
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Tools
?
Projects
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An Internet minute
My Plan
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Thanks for having answered to my
questionnaire!
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A realistic plan
C Part 1: very basic ideas
C Part 2: some work hands on
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Please feel free to answer, interrupt, etc
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Part 1
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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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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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Social interactions
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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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Metaverse?
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Virtual
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Augmented
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Fake
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Holograms
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New,
or more of the same?
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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

Maybe biology could inspire us...
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SO?!
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?
Let's have a look at networks and graphs
Euler 1707 - 1783
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Actors and interactions
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Networks are made of actors and
interactions
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Actors from one or more sets
C People and movies, eg
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Interactions of one or more types
C Follow and retweet, eg
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Actors and interactions may have other
atributes
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Basics of graphs and networks
?
G = (V, E) vertices and edges
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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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Representations of networks
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Matrixes, graphs, edge lists, etc
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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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

Directed/undirected?

Usually not transitive
C a likes b and b likes c does not imply a likes c

Directed > Order (total/partial)
C Rankings, proeminence indexes, etc

Undirected > Equivalence
C Communities, clusters, etc
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Network edges
Global metrics
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Number of vertices 5
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Number of edges 11
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Number of components 1
?
Diameter 2
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Density 0.55
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Centrality Measures
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Degree centrality
C Edges per node
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Closeness centrality
C How close the node is to every other node
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Betweenness centrality
C How many shortest paths go through the node
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Bibliometric + Internet style (quality of edges)
C PageRank, eigenvector
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Which metrics?
?
Depend of the answers we need
C Key nodes
C Ranking
C Influence, contagion
C Communities, etc
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That's what network science is all about
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You have to know the problem
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Part 2
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Which networks?
?
Identifying networks may be not intuitive
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But at the end is all about actors and
interactions
C Vertices = nodes = actors
C Edges = relations = interactions
?
Going through examples is helpful
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Countries, FIFA World Cup
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Co-authors, in a Department
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Characters, Les Misrables
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Dynamics
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Networks may have a temporal dimension
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Interactions C follow, like, share,
mention, retweet, hashtag, etc C occur in
sequence
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Network properties evolve in time
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Datasets
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Where to obtain?
?
Make or get!
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Usually, we know
our subject
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There are many
sites you can visit
C kaggle
C data.world
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polsys.net
MARO musical universe
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My FB, Lost Circles
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Limits?
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Property of data
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Privacy
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Business
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Tools
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People
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Books
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Thank you!
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