2. Topics
•
The explosion of Big Data
•
Data
•
Social Networks
•
Technologies
•
Tools
•
Projects
2SSIIM, 2019/10/07
3. About me
3SSIIM, 2019/10/07
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. AssocLic. EE (6 anos) D.Phil. Prof. Assoc. Director Prof. Assoc
12. Social networks
•
Like, comment, share, cite
•
Dating
•
e-Commerce
•
Payments
•
Digital marketing
•
Political marketing
•
Crime
12SSIIM, 2019/10/07
13. 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...
SSIIM, 2019/10/07 13
20. 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
SSIIM, 2019/10/07 20
21. Representations of networks
•
Matrixes, graphs, edge lists, etc
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
A B
A C
A D
B A
B C
B E
C D
D B
D C
E A
E B
SSIIM, 2019/10/07 21
22. •
Equivalence relations
– Reflexive, symmetric, transitive
– Equivalence classes
•
Order relations (partial, total or linear)
– reflexive, anti-symmetrical, transitive
– Hasse diagrams
– ∀x,y xRy ∨ yRx (total)
SSIIM, 2019/10/07 22
a ≤ b
x taller than y
Be born in the same year
Live in the same street
Binary relations
25. •
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
SSIIM, 2019/10/07 25
Real life relations
26. Global metrics
•
Number of vertexes 5
•
Number of edges 11
•
Number of components 1
•
Diameter 2
•
Density 0.55
SSIIM, 2019/10/07 26
27. Centrality Measures
•
Degree centrality
– Edges per 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
27SSIIM, 2019/10/07
48. Computational Thinking
●
Formulating problems in a way that a
programmable machine can solve them
efficiently
●
Understanding the way programmable
machines operate
●
Understanding of the role computation
can play in solving problems
SSIIM, 2019/10/07 48
49. Ideas?
●
Find and use APIs
●
Graph databases | Neo4j | GQL
●
Visualize citation networks
●
Hashtags co-occurrences (Twitter,
Tumblr)
●
Detect fake/abnormal behaviours
●
Use your imagination!
SSIIM, 2019/10/07 49
