Local clustering coefficient in preferential attachment graphs

In this talk we discuss some properties of generalized preferential attachment models. A general approach to preferential attachment was introduced in [1], where a wide class of models (PA-class) was defined in terms of constraints that are sufficient for the study of the degree distribution and the clustering coefficient.

It was shown in [1] that the degree distribution in all models of the PA-class follows the power law. Also, the global clustering coefficient was analyzed and a lower bound for the average local clustering coefficient was obtained. It was also shown that in preferential attachment models global and average local clustering coefficients behave differently.

In our study we expand the results of [1] by analyzing the local clustering coefficient for the PA-class of models. We analyze the behavior of C(d) which is the average local clustering for vertices of degree d. The value C(d) is defined in the following way. First, the local clustering of a given vertex is defined as the ratio of the number of edges between the neighbors of this vertex to the number of pairs of such neighbors. Then the obtained values are averaged over all vertices of degree d.

[1] L. Ostroumova, A. Ryabchenko, E. Samosvat, Generalized Preferential Attachment: Tunable Power-Law Degree Distribution and Clustering Coefficient, Algorithms and Models for the Web Graph, Lecture Notes in Computer Science Volume 8305, 2013, pp 185-202.