Ranking weighted clustering coefficient in large dynamic graphs

• Published in: World wide web (Bussum) Vol. 20; no. 5; pp. 855 - 883
• Main Authors: Li, Xuefei, Chang, Lijun, Zheng, Kai, Huang, Zi, Zhou, Xiaofang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2017; Springer Nature B.V
• Subjects: Clustering; Coefficients; Computer Science; Database Management; Graphs; Information Systems Applications (incl.Internet); Operating Systems; Ranking; Residential density; Upper bounds; Large dynamic graphs; Top-k search; Node ranking; Clustering coefficient
• DOI: 10.1007/s11280-016-0420-2
• ISSN: 1386-145X

Efficiently searching top- k representative vertices is crucial for understanding the structure of large dynamic graphs. Recent studies show that communities formed by a vertex with high local clustering coefficient and its neighbours can achieve enhanced information propagation speed as well as disease transmission speed. However, local clustering coefficient, which measures the cliquishness of a vertex in its local neighbourhood, prefers vertices with small degrees. To remedy this issue, in this paper we propose a new ranking measure, weighted clustering coefficient (WCC) of vertices, by integrating both local clustering coefficient and degree. WCC not only inherits the properties of local clustering coefficient but also approximately measures the density (i.e., average degree) of its neighbourhood subgraph. Thus, vertices with higher WCC are more likely to be representative. We study efficiently computing and monitoring top- k representative vertices based on WCC over large dynamic graphs. To reduce the search space, we propose a series of heuristic upper bounds for WCC to prune a large portion of disqualifying vertices from the search space. We also develop an approximation algorithm by utilizing Flajolet-Martin sketch to trade acceptable accuracy for enhanced efficiency. An efficient incremental algorithm dealing with frequent updates in dynamic graphs is explored as well. Extensive experimental results on a variety of real-life graph datasets demonstrate the efficiency and effectiveness of our approaches.