Name
Abstract | ||
|---|---|---|
It is difficult to detect and evaluate the number of communities in complex networks, especially when the situation involves an ambiguous boundary between the inner-and inter-community densities. In this paper, discrete nodal domain theory is used to provide a criterion to determine how many communities a network has and how to partition these communities by means of topological structure and geometric characterization. By capturing the signs of the Laplacian eigenvectors, we separate the network into several reasonable clusters. The method leads to a fast and effective algorithm with application to a variety of real network data sets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1088/1742-5468/2012/02/P02012 | JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT |
Keywords | Field | DocType |
analysis of algorithms,clustering techniques | Cluster (physics),Topology,Mathematical optimization,Quantum mechanics,Analysis of algorithms,Domain theory,Network data,Complex network,Partition (number theory),Mathematics,Eigenvalues and eigenvectors,Laplace operator | Journal |
Volume | Issue | ISSN |
abs/1201.5767 | 2 | 1742-5468 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bian He | 1 | 0 | 0.34 |
| Lei Gu | 2 | 38 | 7.66 |
| Xiao-Dong Zhang | 3 | 38 | 4.97 |