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Clustering Large Undirected Graphs on External Memory
| Content Provider | ACM Digital Library |
|---|---|
| Author | Rao, Weixiong Xiao, Jiakai Liu, Qin |
| Abstract | Traditional graph clustering methods perform poorly on real world power-law graphs out of core. To tackle this challenge, in this paper, we propose an algorithm to cluster such large power law graphs in case of small memory size. In the proposed method, clusters (connected components) are formed by removing top degree nodes (hubs) from the graph. In order to minimize the time for selecting hubs, a divide and conquer strategy is adopted so hubs are selected locally. Compared with the state of art Slashbrun [slashburn2], the proposed algorithm can achieve 30\$\times\$ faster running time with about 6% more storage cost. |
| Starting Page | 25 |
| Ending Page | 30 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450335348 |
| DOI | 10.1145/2798087.2798089 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-09-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Graph clustering Social network Power law |
| Content Type | Text |
| Resource Type | Article |
