The document discusses visualizing and analyzing networks. It begins with an introduction to the speaker and overview of topics to be covered, including the explosion of big data, social networks, and network analysis tools. The bulk of the document outlines a basic plan to introduce network concepts, including defining networks as collections of actors and interactions, examples of network representations and metrics, and discussing analyzing dynamics and obtaining network data from real world examples.
2. 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
7. 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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13. 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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16. 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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17. Basics of graphs and networks
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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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18. 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
19.
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
20. 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
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Diameter 2
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Density 0.55
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21. 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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22. Which metrics?
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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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24. Which networks?
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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
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Going through examples is helpful
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29. 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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