Fast Computation of Dense Temporal Subgraphs

Dense subgraph discovery has proven useful in various applications of temporal networks. We focus on a special class of temporal networks whose nodes and edges are kept fixed, but edge weights constantly and regularly vary with timestamps. However, finding dense subgraphs in temporal networks is nontrivial, and its state of the art solution uses a filter-and-verification framework, which is not scalable on large temporal networks. In this study, we propose a data-driven approach to finding dense subgraphs in large temporal networks with T timestamps. (1) We first develop a data-driven approach employing hidden statistics to identifying k time intervals, instead of T (T + 1)/2 ones (k is typically much smaller than T), which strikes a balance between quality and efficiency. (2) After proving that the problem has no constant factor approximation algorithms, we design better heuristic algorithms to attack the problem, by building the connection of finding dense subgraphs with a variant of the Prize Collecting Steiner Tree problem. (3) Finally, we have conducted an extensive experimental study to demonstrate the effectiveness and efficiency of our approach, using real-life and synthetic data.