Persistent Community Search Over Temporal Bipartite Graphs.

Bipartite graphs are commonly used to model the relationship between two types of entities, and community search over them has recently gained much attention. However, in real-world scenarios, edges often contain temporal information, such as the timestamp of a user visiting a location at time instant t . Unfortunately, most previous studies have focused on finding communities without temporal information. In this paper, we study the problem of persistent community search over temporal bipartite graphs. We propose a novel persistent community model called ( θ , τ )- persistent ( α , β )-core to capture the persistence of a community, where α and β are the lower bound degree of each layer in the projected graph of the persistent community, and θ and τ represent the minimum and maximum temporal duration for the entire ( α , β )-core. We have proved the NP-hardness of the problem. To solve this problem, we propose a PCSearch algorithm using the branch-reduce-and-bound strategy divided into four sub-algorithms. Finally, we conduct extensive experiments over several real-world temporal bipartite networks to demonstrate the efficiency and effectiveness of our proposed algorithm.