Abstract
Given a graph over which defects, viruses, or contagions spread, leveraging a set of highly correlated subgraphs is an appealing research area with many applications. However, the challenges abound. Firstly, an initial defect in one node can cause different defects in other nodes. Second, while the time is the most significant medium to understand diffusion processes, it is not clear when the members of a subgraph may change. Third, given a pair of nodes, a contagion can spread in both directions. Previous works only consider the sequential time-window and suppose that the contagion may spread from one node to the other during a predefined time span. But the propagation can differ in various temporal dimensions (e.g. hours and days). Therefore, we propose a framework that takes both sequential and multi-aspect attributes of the time into consideration. Moreover, we devise an empirical model to estimate how frequently the subgraphs may reshape. Experiment show that our framework can effectively leverage the reshaping subgraphs.