Abstract
Given a set AL of community detection algorithms and a graph G as inputs, we propose two ensemble methods EnDisCo and MeDOC that (respectively) identify disjoint and overlapping communities in G. EnDisCo transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. MeDOC groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare EnDisCo and MeDOC with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, MeDOC is the first ensemble approach for overlapping community detection.
Original language | English (US) |
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Title of host publication | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
Editors | Ravi Kumar, James Caverlee, Hanghang Tong |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 73-80 |
Number of pages | 8 |
ISBN (Electronic) | 9781509028467 |
DOIs | |
State | Published - Nov 21 2016 |
Externally published | Yes |
Event | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: Aug 18 2016 → Aug 21 2016 |
Publication series
Name | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
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Other
Other | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
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Country/Territory | United States |
City | San Francisco |
Period | 8/18/16 → 8/21/16 |
Funding
Parts of this work were funded by ARO Grants W911NF-16-1-0342, W911NF1110344, W911NF1410358, by ONR Grant N00014-13-1-0703, and Maryland Procurement Ofce under Contract No. H98230-14-C-0137
ASJC Scopus subject areas
- Computer Networks and Communications
- Sociology and Political Science
- Communication