Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks

Abstract

Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex to act as a communication "bridge" between other vertices in the network, is widely used in many practical applications. With the advent of large heterogeneous information networks (HINs) which contain multiple types of vertices and edges like movie or bibliographic networks, it is essential to study BC computation on HINs. However, existing works about BC mainly focus on homogeneous networks. In this paper, we are the first to study a specific type of vertices' BC on HINs, e.g., find which vertices with type A are important bridges to the communication between other vertices also with type A? We advocate a meta path-based BC framework on HINs and formalize both coarse-grained and fine-grained BC (cBC and fBC) measures under the framework. We propose a generalized basic algorithm which can apply to computing not only cBC and fBC but also their variants in more complex cases. We develop several optimization strategies to speed up cBC or fBC computation by network compression and breadth-first search directed acyclic graph (BFS DAG) sharing. Experiments on several real-world HINs show the significance of cBC and fBC, and the effectiveness of our proposed optimization strategies.