A graph clustering method for community detection in complex networks
| Autor: | Facun Zhang, YingAn Cui, Junhuai Li, Hongfang Zhou, Jin Li |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Power graph analysis Physics Degree (graph theory) Node (networking) Correlation clustering Statistical and Nonlinear Physics 02 engineering and technology Complex network Similarity measure computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Similarity (network science) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer Clustering coefficient |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 469:551-562 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2016.11.015 |
| Popis: | Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect