Cognitive and Smart Adaptation in Computer-Communication Networks

2006 ◽  
Vol 36 (6) ◽  
pp. 1216-1217
Keyword(s):  
Communication Networks ◽  
Computer Communication Networks ◽  
Computer Communication

Rational Controllers in Computer/Communication Networks

2008 ◽  
Vol 41 (2) ◽  
pp. 15427-15432
Author(s):  
Pierre T. Kabamba ◽  
Wen-Chiao Lin ◽  
Semyon M. Meerkov ◽  
Choon Yik Tang
Keyword(s):  
Communication Networks ◽  
Computer Communication Networks ◽  
Computer Communication ◽  
Rational Controllers

Graph Mining Techniques

2013 ◽  
pp. 446-464 ◽  
Author(s):  
Ana Paula Appel ◽  
Christos Faloutsos ◽  
Caetano Traina Junior
Keyword(s):  
Social Networks ◽  
Communication Networks ◽  
Real World ◽  
Biological Networks ◽  
Graph Mining ◽  
Recommendation Systems ◽  
Computer Communication Networks ◽  
Computer Communication ◽  
Good Pattern ◽  
As Graph

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A large amount of graph patterns, as well as graph generator models that mimic such patterns have been proposed over the last years. However, a deep and recurring question still remains: “What is a good pattern?” The answer is related to finding a pattern or a tool able to help distinguishing between actual real-world and fake graphs. Here we explore the ability of ShatterPlots, a simple and powerful algorithm to tease out patterns of real graphs, helping us to spot fake/masked graphs. The idea is to force a graph to reach a critical (“Shattering”) point, randomly deleting edges, and study its properties at that point.

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