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Well-known examples of applying this approach are the Internet graph, the Web graph, and the Call graph.
However, the structure of the Internet graph at the router level can be better captured by using multiresolution methods.
Since the revolution of the computer technologies and the Internet, graph data have become more and more important because many of the "big" data are naturally formed in a graph structure or can be transformed into graphs.
A premature conclusion might state that the Internet graph does not have communities at the router level, or at least that they are not related with the Autonomous Systems.
The right picture in Figure 3 shows the evolution of classical modularity Q for the Internet graph as a function of the resolution t, i.e. as the algorithm evolves (from right to left).
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In communication networks, the scaled link density does not depend on community size, whereas in Internet graphs large communities appear somewhat denser.
While Facebook has positioned itself to be the Internet's social graph of friendships and relationships, MySpace has evolved to become a media and entertainment destination.
In this work we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure.
In a first stage, we analysed the community structure of the Internet router-level graph by applying several community detection methods: Infomap, by Rosvall and Bergstrom, based in the description length [21].
Materials | Student journals, computers with Internet access, scientific or graphing calculators, graph paper.
By overlapping different "social graphs," Internet jargon for one's connections on a social network, it may be possible to reveal identities of people who have taken all precautions to remain anonymous.
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