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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wen Haw Chong Loo Nin Teow |
| Copyright Year | 2013 |
| Description | Author affiliation: DSO Nat. Labs., Singapore, Singapore (Wen Haw Chong; Loo Nin Teow) |
| 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. |
| Starting Page | 750 |
| Ending Page | 757 |
| File Size | 782189 |
| Page Count | 8 |
| File Format | |
| ISBN | 9786058631113 |
| e-ISBN | 9781479902842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-09 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Communities Optimization Merging Computational efficiency Social network services Image edge detection Accuracy Complex Networks Community Detection Incremental Update Modularity |
| Content Type | Text |
| Resource Type | Article |
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