Flow Computation in Temporal Interaction Networks

Temporal interaction networks capture the history of activities between entities along a timeline. At each interaction, some quantity of data (money, information, traffic) flows from one vertex of the network to another. Flow-based analysis can reveal important information, such as unusually large money transfers in a part of a financial transaction network. In this paper, we introduce the flow computation problem between two vertrices in an interaction network. We propose and study two models of flow computation, one based on a greedy flow transfer assumption and one that finds the maximum possible flow. We show that the greedy flow computation problem can be easily solved by a single scan of the interactions in time order. For the harder maximum flow problem, we propose precomputation and simplification approaches that can greatly reduce its complexity in practice. We also approach the problem of flow pattern enumeration in interaction networks and propose an effective path indexing technique. We evaluate our algorithms using real datasets. The results demonstrate the efficiency and scalability of our algorithms.