Distributed time-respecting flow graph pattern matching on temporal graphs

Graph pattern matching, one of the most fundamental graph problems, has been extensively investigated in the literature. Nonetheless, existing efforts mostly focus on general graphs without time information, few studies concentrate on temporal graphs, where a relationship between two vertices takes place at a specific moment and lingers for some time. Moreover, real-world temporal networks become increasingly large, and are usually distributed over multiple machines. These foster the need for evaluating pattern matching on distributed temporal graphs. In this paper, we propose a new notion so-called time-respecting flow graph , in which all paths spreading from one vertex to another are time-respecting (i.e., a series of contacts with non-decreasing time), and one vertex is distinguished as the root from which other vertices can be reached via a time-respecting path. Based on this, we explore the problem of distributed time-respecting flow graph pattern matching on temporal graphs , which could be applied in many fields such as epidemiology, social media, national security, communication, to name just a few. We present a distributed baseline algorithm based on GraphX as well as an optimized algorithm that utilizes the properties of time-respecting flow graph and the analyses of distributed algorithms to boost efficiency. Extensive experimental evaluation using both real and synthetic data sets demonstrates the efficiency and scalability of our proposed algorithms.