The data-driven null models for information dissemination tree in social networks

Abstract

For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.