OLAP (On-line Analytical Processing) can provide users with aggregate results from different perspectives and granularities. With the advent of heterogeneous information networks that consist of multi-type, interconnected nodes, such as bibliographic networks and knowledge graphs, it is important to study flexible aggregation in such networks. The aggregation results by existing work are limited to one type of node, which cannot be applied to aggregation on multi-type nodes, and relations in large-scale heterogeneous information networks. In this paper, we investigate the flexible aggregation problem on large-scale heterogeneous information networks, which is defined on multi-type nodes and relations. Moreover, by considering both attributes and structures, we propose a novel function based on graph entropy to measure the similarities of nodes. Further, we prove that the aggregation problem based on the function is NP-hard. Therefore, we develop an efficient heuristic algorithm for aggregation in two phases: informational aggregation and structural aggregation. The algorithm has linear time and space complexity. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
A Community Detection Algorithm for Heterogeneous Information Networks
