This document discusses research analyzing criminal networks in Brazil using network analysis techniques. The researchers collected data on over 15,000 criminal individuals and their relationships, creating a network of over 9,000 nodes and 19,000 links. Analyzing the network, they identified 93 distinct criminal modules. The researchers then applied various attack strategies to the network, such as targeting high-betweenness or high-degree nodes, and found that targeting inter-module links using a module-based attack strategy was the most effective at fragmenting the network.

1. Requião da Cunha B,
PhD student - UFRGS
Brazilian Federal Police Agent
Fast Fragmentation of Networks
Using Module-Based Attacks
http://dx.doi.org/10.1371/journal.pone.0142824
Gonçalves Sebastia,
Proffesor - UFRGS
González-Avella JC,
IFISC.

4. Data collection;
• Standardization of qualifiers.

5. Standardization of qualifiers.

6. Criminal
N = 15.887
Employ
N = 2038
Familiar
N = 8495
Doubts
N = 1040
Locales
N = 508
+ +
+
+
+

7. Snapshot of organized Criminal groups investigated by the Brazilian Federal Police
between April and August Of 2013

8. Snapshot of organized Criminal groups investigated by the Brazilian Federal Police
between April and August Of 2013

13. Module-Based Attacks

14. Number of Nodes = 9888
Number of links = 19744
Avg path lenght = 14.43.
Clustering Coeficient = 0.428
Aveg. degree k= 3.99
Brazilian Criminal Network

15. Number of Nodes = 9888
Number of links = 19744
Avg path lenght = 14,43.
Clustering Coeficient = 0.428
Aveg. degree k= 3.99
Brazilian Criminal Network
Number of modules = 93
Q = 0.95821

16. Brazilian Criminal Inter-Comunity Network
Number of modules = 93
Inter-modules links = 176
Q = 0.95821
52-(35)-23
links
Nodes = 196 Nodes = 77
9
Nodes = 183
(3 links)

17. 52 [35] 23
88 [27] 86
86 [19] 24
33 [13] 86
30 [11] 23
23 [11] 31
21 [11] 66
37 [10] 21
6 [10] 37
21 [9] 89
28 [8] 63
6 [8] 21
74 [8] 21
23 [8] 8
86 [7] 67
21 [7] 92
86 [7] 28
86 [7] 72
52 [6] 30
52 [6] 42
28 [5] 47
21 [5] 41
79 [4] 70
6 [4] 66
32 [4] 54
79 [4] 86
60 [4] 86
52 [4] 31
42 [4] 23
38 [4] 92
36 [3] 47
63 [3] 33
23 [3] 4
49 [3] 15
24 [3] 88
54 [3] 33
5 [3] 77
5 [3] 23
44 [3] 49
9 [3] 52
21 [3] 46
47 [3] 14
15 [3] 21
9 [3] 43
8 [2] 31
7 [2] 37
16 [2] 32
50 [2] 19
72 [2] 24
15 [2] 32
79 [2] 33
85 [2] 19
28 [2] 54
11 [2] 63
33 [2] 70
70 [2] 86
67 [2] 24
82 [2] 21
59 [2] 42
21 [2] 2
32 [2] 2
33 [2] 73
67 [2] 88
15 [2] 44
46 [2] 37
54 [2] 86
33 [2] 62
52 [2] 81
7 [2] 15
57 [2] 54
91 [2] 6
59 [1] 35
85 [1] 67
19 [1] 20
46 [1] 68
86 [1] 47
12 [1] 9
25 [1] 49
15 [1] 3
33 [1] 2
32 [2] 2
33 [2] 73
67 [2] 88
15 [2] 44
46 [2] 37
54 [2] 86
33 [2] 62
52 [2] 81
7 [2] 15
57 [2] 54
91 [2] 6
59 [1] 35
85 [1] 67
19 [1] 20
46 [1] 68
86 [1] 47
12 [1] 9
25 [1] 49
15 [1] 3
33 [1] 2
Ci CjLink Ci CjLink Ci CjLink Ci CjLink Ci CjLink

18. Removed Links
Removed Nodes

23. Fig 2. Comparison between the effect of betweenness-based attack, degree-based attack,
longest path attack, random attack, and module-based attack network.

26. Fig 4. Size of the biggest connected component in terms of the initial size, σ, as function of
fraction of removed edges, ρ.

27. Fig 6. Overall efficiency gain (η) of the MBA method
relative to the CBA method as function of modularity,
Q, for nodes and edges removal.
The vertical axis is in logarithmic scale and the
horizontal axis is linear. The networks attacked are
Facebook (FB), Twitter (TW), Google Plus (G+), US
power grid (PG), Euro road (ER), Open flights (OF),
US airports (UA), Yeast protein (YP), H pylori (HP),
and C elegans (CE).
Modularity and the fraction of
bridging links.

28. Data Analisys Tools:
Python,
Numpy
Scipy
Matplotlib
Networkx
Cytoscape
R