Research Article
Identifying Local Clustering Structures of Evolving Social Networks Using Graph Spectra (Short Paper)
@INPROCEEDINGS{10.1007/978-3-030-12981-1_11, author={Bo Jiao and Yiping Bao and Jin Wang}, title={Identifying Local Clustering Structures of Evolving Social Networks Using Graph Spectra (Short Paper)}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Social networks Clustering coefficient Weighted spectral distribution Normalized Laplacian spectrum}, doi={10.1007/978-3-030-12981-1_11} }- Bo Jiao
Yiping Bao
Jin Wang
Year: 2019
Identifying Local Clustering Structures of Evolving Social Networks Using Graph Spectra (Short Paper)
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_11
Abstract
The clustering coefficient has been widely used for identifying the local structure of networks. In this paper, the weighted spectral distribution with 3-cycle (WSD3) that is similar (but not equal) to the clustering coefficient is studied on evolving social networks. It is demonstrated that the ratio of the WSD3 to the network size (i.e., the node number) provides a more sensitive discrimination for the size-independent local structure of social networks in contrast to the clustering coefficient. Moreover, the difference of the WSD3’s performances on social networks and communication networks is investigated, and it is found that the difference is induced by the different symmetrical features of the normalized Laplacian spectral densities on these networks.