dorsal/arxiv
View SchemaDistance, dissimilarity index, and network community structure
| Authors | Haijun Zhou |
|---|---|
| Categories | |
| ArXiv ID | physics/0302032 |
| URL | https://arxiv.org/abs/physics/0302032 |
| DOI | 10.1103/PhysRevE.67.061901 |
| Journal | Physical Review E 67: 061901 (2003) |
Abstract
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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"abstract": "We address the question of finding the community structure of a complex\nnetwork. In an earlier effort [H. Zhou, {\\em Phys. Rev. E} (2003)], the concept\nof network random walking is introduced and a distance measure defined. Here we\ncalculate, based on this distance measure, the dissimilarity index between\nnearest-neighboring vertices of a network and design an algorithm to partition\nthese vertices into communities that are hierarchically organized. Each\ncommunity is characterized by an upper and a lower dissimilarity threshold. The\nalgorithm is applied to several artificial and real-world networks, and\nexcellent results are obtained. In the case of artificially generated random\nmodular networks, this method outperforms the algorithm based on the concept of\nedge betweenness centrality. For yeast\u0027s protein-protein interaction network,\nwe are able to identify many clusters that have well defined biological\nfunctions.",
"arxiv_id": "physics/0302032",
"authors": [
"Haijun Zhou"
],
"categories": [
"physics.bio-ph",
"cond-mat.stat-mech",
"physics.data-an",
"q-bio.MN"
],
"doi": "10.1103/PhysRevE.67.061901",
"journal_ref": "Physical Review E 67: 061901 (2003)",
"title": "Distance, dissimilarity index, and network community structure",
"url": "https://arxiv.org/abs/physics/0302032"
},
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