Efficient algorithms for discovering importance-based communities in large web-scale networks
| dc.contributor.author | Wei, Ran | |
| dc.contributor.supervisor | Thomo, Alex | |
| dc.date.accessioned | 2017-08-18T18:25:57Z | |
| dc.date.available | 2017-08-18T18:25:57Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017-08-18 | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | k-core is a notion capturing the cohesiveness of a subgraph in a social network graph. Most of the current research work only consider pure network graphs and neglect an important property of the nodes: influence. Li, Qin, Yu, and Mao introduced a novel community model called k-influential community which is based on the concept of k-core enhanced with node influence values. In this model, we are interested not only in subgraphs that are well-connected but also have a high lower-bound on their influence. More precisely, we are interested in finding top r (with respect to influence), k-core communities. We present novel approaches that provide an impressive scalability in solving the problem for graphs of billions of edges using only a consumer-grade machine. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/8432 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | k-core | en_US |
| dc.subject | influential community | en_US |
| dc.subject | large network | en_US |
| dc.title | Efficient algorithms for discovering importance-based communities in large web-scale networks | en_US |
| dc.type | Thesis | en_US |