Network community detection based on spectral clustering

In recent years, spectral clustering based on the spectral graph theory has become one of the most popular clustering algorithms. It is easy to implement and is widely used in the domain of pattern recognition. In this paper, a new method is proposed to estimate the number of communities based on spectral clustering. The conductivity function and the accuracy are used to evaluate the quality of community detection. Experimental results on Zachary Karate Club show that the proposed method yields a high accuracy and effectiveness.

[1]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[2]  Jianyong Wang,et al.  Parallel community detection on large networks with propinquity dynamics , 2009, KDD.

[3]  Yun Chi,et al.  Combining link and content for community detection: a discriminative approach , 2009, KDD.

[4]  Yanchi Liu,et al.  Community detection in graphs through correlation , 2014, KDD.

[5]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Philip S. Yu,et al.  Hierarchical, Parameter-Free Community Discovery , 2008, ECML/PKDD.

[7]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[8]  Steve Gregory,et al.  A Fast Algorithm to Find Overlapping Communities in Networks , 2008, ECML/PKDD.

[9]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.