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| Content Provider | Springer Nature Link |
|---|---|
| Author | Cordeiro, Mário Sarmento, Rui Portocarrero Gama, João |
| Copyright Year | 2016 |
| Abstract | The amount and the variety of data generated by today’s online social and telecommunication network services are changing the way researchers analyze social networks. Facing fast evolving networks with millions of nodes and edges are, among other factors, its main challenge. Community detection algorithms in these conditions have also to be updated or improved. Previous state-of-the-art algorithms based on the modularity optimization (i.e. Louvain algorithm), provide fast, efficient and robust community detection on large static networks. Nonetheless, due to the high computing complexity of these algorithms, the use of batch techniques in dynamic networks requires to perform network community detection for the whole network in each one of the evolution steps. This fact reveals to be computationally expensive and unstable in terms of tracking of communities. Our contribution is a novel technique that maintains the community structure always up-to-date following the addition or removal of nodes and edges. The proposed algorithm performs a local modularity optimization that maximizes the modularity gain function only for those communities where the editing of nodes and edges was performed, keeping the rest of the network unchanged. The effectiveness of our algorithm is demonstrated with the comparison to other state-of-the-art community detection algorithms with respect to Newman’s Modularity, Modularity with Split Penalty, Modularity Density, number of detected communities and running time. |
| Starting Page | 1 |
| Ending Page | 20 |
| Page Count | 20 |
| File Format | |
| ISSN | 18695450 |
| Journal | Social Network Analysis and Mining |
| Volume Number | 6 |
| Issue Number | 1 |
| e-ISSN | 18695469 |
| Language | English |
| Publisher | Springer Vienna |
| Publisher Date | 2016-03-24 |
| Publisher Place | Vienna |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Dynamic community detection Large-scale networks analysis Modularity Evolving networks Data Mining and Knowledge Discovery Applications of Graph Theory and Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology |
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