Title:
Detecting Communities from Given Seeds in Social Networks
Detecting Communities from Given Seeds in Social Networks
| dc.contributor.author | Riedy, Jason | |
| dc.contributor.author | Bader, David A. | |
| dc.contributor.author | Jiang, Karl | |
| dc.contributor.author | Pande, Pushkar | |
| dc.contributor.author | Sharma, Richa | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Computational Science and Engineering | |
| dc.date.accessioned | 2011-02-23T23:10:40Z | |
| dc.date.available | 2011-02-23T23:10:40Z | |
| dc.date.issued | 2011-02-22 | |
| dc.description.abstract | Analyzing massive social networks challenges both high-performance computers and human under- standing. These massive networks cannot be visualized easily, and their scale makes applying complex analysis methods computationally expensive. We present a region-growing method for finding a smaller, more tractable subgraph, a community, given a few example seed vertices. Unlike existing work, we focus on a small number of seed vertices, from two to a few dozen. We also present the first comparison between five algorithms for expanding a small seed set into a community. Our comparison applies these algorithms to an R-MAT generated graph component with 240 thousand vertices and 32 million edges and evaluates the community size, modularity, Kullback-Leibler divergence, conductance, and clustering coefficient. We find that our new algorithm with a local modularity maximizing heuristic based on Clauset, Newman, and Moore performs very well when the output is limited to 100 or 1000 vertices. When run without a vertex size limit, a heuristic from McCloskey and Bader generates communities containing around 60% of the graph's vertices and having a small conductance and modularity appropriate to the result size. A personalized PageRank algorithm based on Andersen, Lang, and Chung also performs well with respect to our metrics. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/36980 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | CSE Technical Reports ; GT-CSE-11-01 | en_US |
| dc.subject | Conductance | en_US |
| dc.subject | Modularity | en_US |
| dc.subject | Random walk | en_US |
| dc.subject | Region-growing algorithm | en_US |
| dc.subject | Seed set | en_US |
| dc.subject | Social networks | en_US |
| dc.subject | Vertices | en_US |
| dc.title | Detecting Communities from Given Seeds in Social Networks | en_US |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Computational Science and Engineering | |
| local.relation.ispartofseries | College of Computing Technical Report Series | |
| local.relation.ispartofseries | School of Computational Science and Engineering Technical Report Series | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1 | |
| relation.isSeriesOfPublication | 35c9e8fc-dd67-4201-b1d5-016381ef65b8 | |
| relation.isSeriesOfPublication | 5a01f926-96af-453d-a75b-abc3e0f0abb3 |