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.
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Acknowledgement
This research has been supported by the Open Fund Project of National Engineering Laboratory for Big Data Application on Improving Government Governance Capabilities.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Jiao, B., Bao, Y., Wang, J. (2019). Identifying Local Clustering Structures of Evolving Social Networks Using Graph Spectra (Short Paper). In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_11
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DOI: https://doi.org/10.1007/978-3-030-12981-1_11
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