Community detection in network analysis: a survey
| dc.contributor.advisor | Lin, Lizhen, Ph.D. | |
| dc.contributor.committeeMember | Keitt, Timothy | |
| dc.creator | Zhang, Lingjia | |
| dc.date.accessioned | 2016-10-13T19:11:31Z | |
| dc.date.accessioned | 2018-01-22T22:30:46Z | |
| dc.date.available | 2016-10-13T19:11:31Z | |
| dc.date.available | 2018-01-22T22:30:46Z | |
| dc.date.issued | 2016-05 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2016-10-13T19:11:31Z | |
| dc.description.abstract | The existence of community structures in networks is not unusual, including in the domains of sociology, biology, and business, etc. The characteristic of the community structure is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. In academia, there is a surge in research efforts on community detection in network analysis, especially in developing statistically sound methodologies for exploring, modeling, and interpreting these kind of structures and relationships. This survey paper aims to provide a brief review of current applicable statistical methodologies and approaches in a comparative manner along with metrics for evaluating graph clustering results and application using R. At the end, we provide promising future research directions. | |
| dc.description.department | Statistics | |
| dc.format.mimetype | application/pdf | |
| dc.identifier | doi:10.15781/T2NP1WK8G | |
| dc.identifier.uri | http://hdl.handle.net/2152/41634 | |
| dc.subject | Network analysis | |
| dc.subject | Community detection | |
| dc.subject | Clustering | |
| dc.title | Community detection in network analysis: a survey | |
| dc.type | Thesis | |
| dc.type.material | text |