Querying historical K-cores in large temporal graphs

Yuanhang Yu, Dong Wen, Michael Yu, Lu Qin, Ying Zhang, Wenjie Zhang, Xuemin Lin

Abstract: Many real-world relationships between entities can be modeled as temporal graphs, where each edge is associated with a timestamp or a time interval representing its occurrence. The k-core is a fundamental model used to capture cohesive subgraphs in a simple graph and have drawn much research attention over the last decade. Despite widespread research, none of the existing works support the efficient querying of historical k-cores in temporal graphs. In this paper, given an integer k and a time window, we study the problem of computing all the nodes belonging to the k-core in the graph snapshot over the time window. We propose an index-based solution and several pruning strategies to reduce the index size. We design a novel algorithm to construct this index, whose running time is linear to the final index size. We also propose algorithms to maintain our index given the continuous arrival of new edges. Lastly, we conducted extensive experiments on several real-world temporal graphs to show the high effectiveness of our index-based solution.