Community detection and unveiling of hierarchy in networks: a density-based clustering approach
Abstract
Abstract The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuits to cybersecurity. We present a statistical mechanics approach that uses a normalized Gaussian function which captures the impact of a node within its neighborhood and leads to a density-ranking of nodes by considering the distance between nodes as punishment. A hill-climbing procedure is applied to determine the density attractors and identify the unique parent (leader) of each member as well as the group leader. This organization of the nodes results in a tree-like network with multiple clusters, the community tree. The method is tested using synthetic networks generated by the LFR benchmarking algorithm for network sizes between 500 and 30,000 nodes and mixing parameter between 0.1 and 0.9 . Our results show a reasonable agreement with the LFR results for low to medium values of the mixing parameter and indicate a very mild dependence on the size of the network.
- Publication Date:
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- OSTI Identifier:
- 1619421
- Grant/Contract Number:
- NA 0002686; 2017-2007
- Resource Type:
- Published Article
- Journal Name:
- Applied Network Science
- Additional Journal Information:
- Journal Name: Applied Network Science Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2364-8228
- Publisher:
- Springer Science + Business Media
- Country of Publication:
- Switzerland
- Language:
- English
Citation Formats
Felfli, Zineb, George, Roy, Shujaee, Khalil, and Kerwat, Mohamed. Community detection and unveiling of hierarchy in networks: a density-based clustering approach. Switzerland: N. p., 2019.
Web. doi:10.1007/s41109-019-0216-2.
Felfli, Zineb, George, Roy, Shujaee, Khalil, & Kerwat, Mohamed. Community detection and unveiling of hierarchy in networks: a density-based clustering approach. Switzerland. https://doi.org/10.1007/s41109-019-0216-2
Felfli, Zineb, George, Roy, Shujaee, Khalil, and Kerwat, Mohamed. Tue .
"Community detection and unveiling of hierarchy in networks: a density-based clustering approach". Switzerland. https://doi.org/10.1007/s41109-019-0216-2.
@article{osti_1619421,
title = {Community detection and unveiling of hierarchy in networks: a density-based clustering approach},
author = {Felfli, Zineb and George, Roy and Shujaee, Khalil and Kerwat, Mohamed},
abstractNote = {Abstract The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuits to cybersecurity. We present a statistical mechanics approach that uses a normalized Gaussian function which captures the impact of a node within its neighborhood and leads to a density-ranking of nodes by considering the distance between nodes as punishment. A hill-climbing procedure is applied to determine the density attractors and identify the unique parent (leader) of each member as well as the group leader. This organization of the nodes results in a tree-like network with multiple clusters, the community tree. The method is tested using synthetic networks generated by the LFR benchmarking algorithm for network sizes between 500 and 30,000 nodes and mixing parameter between 0.1 and 0.9 . Our results show a reasonable agreement with the LFR results for low to medium values of the mixing parameter and indicate a very mild dependence on the size of the network.},
doi = {10.1007/s41109-019-0216-2},
journal = {Applied Network Science},
number = 1,
volume = 4,
place = {Switzerland},
year = {Tue Oct 22 00:00:00 EDT 2019},
month = {Tue Oct 22 00:00:00 EDT 2019}
}
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