Name
Abstract | ||
|---|---|---|
In the analysis of real world networks, it is often of interest to partition nodes into groups referred to as communities, whereby each community is densely connected and different communities are sparsely connected to one another. While community detection on static networks has been extensively researched on, updating community structures efficiently and accurately on evolving networks is a relatively new research area. In this paper, we discuss the inadequacies of previous techniques as well as justify the need for a new class of techniques that can handle complex batch changes in networks. We then propose one such incremental technique. Compared to earlier work, the proposed technique is much more efficient in scenarios where a network evolves significantly while maintaining a high level of accuracy. Experiments on both artificial and real world networks validate the utility of the proposed technique. |
Year | Venue | Keywords |
|---|---|---|
2013 | Fusion | complex networks,incremental batch technique,community structures,incremental update,modularity,community detection,static networks,network theory (graphs),optimization,merging,accuracy |
Field | DocType | ISBN |
Computer science,Evolving networks,Artificial intelligence,Clique percolation method,Machine learning,Distributed computing | Conference | 978-605-86311-1-3 |
Citations | PageRank | References |
2 | 0.37 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Haw Chong | 1 | 5 | 1.51 |
| Loo-Nin Teow | 2 | 103 | 17.29 |