Effective and Efficient Truss Computation over Large Heterogeneous Information Networks

Recently, the topic of truss computation has gained plenty of attention, where the k-truss of a graph is the maximum subgraph in which each edge participates in at least (k-2) triangles. Existing solutions mainly focus on homogeneous networks, where vertices are of the same type, and thus cannot be applied to heterogeneous information networks which consist of multi-typed and interconnected objects, such as the bibliographic networks and knowledge graphs. In this paper, we study the problem of truss computation over HINs, which aims to find groups of vertices that are of the same type and densely connected. To model the relationship between two vertices of the same type, we adopt the well-known concept of meta-path, which is a sequence of vertex types and edge types between two given vertex types. We then introduce two kinds of HIN triangles for three vertices, regarding a specific meta-path P. The first one requires that each pair of vertices is connected by an instance of P, while the second one also has such a connectivity constraint but further needs that the three instances of P form a circle. Based on these two kinds of triangles, we propose two HIN truss models respectively. We further develop efficient truss computation algorithms. We have performed extensive experiments on five real large HINs, and the results show that the proposed solutions are highly effective and efficient.