Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distance, dissimilarity index, and network community structure   We address the question of finding the community structure of a complex
network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept
of network random walking is introduced and a distance measure defined. Here we
calculate, based on this distance measure, the dissimilarity index between
nearest-neighboring vertices of a network and design an algorithm to partition
these vertices into communities that are hierarchically organized. Each
community is characterized by an upper and a lower dissimilarity threshold. The
algorithm is applied to several artificial and real-world networks, and
excellent results are obtained. In the case of artificially generated random
modular networks, this method outperforms the algorithm based on the concept of
edge betweenness centrality. For yeast's protein-protein interaction network,
we are able to identify many clusters that have well defined biological
functions.

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The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
2
The categories of this paper are: math
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an