Efficient temporal shortest path queries on evolving social graphs

Graph-like data appears in many applications, such as social networks, internet hyperlinks, roadmaps, etc. and in most cases, graphs are dynamic, evolving through time. In this work, we study the problem of efficient shortest-path query evaluation on evolving social graphs. Our shortest-path queries are "temporal": they can refer to any time-point or time-interval in the graph's evolution, and corresponding valid answers should be returned. To efficiently support this type of temporal query, we extend the traditional Dijkstra's algorithm to compute shortest-path distance(s) for a time-point or a time-interval. To speed up query processing, we explore preprocessing index techniques such as Contraction Hierarchies (CH). Moreover, we examine how to maintain the evolving graph along with the index by utilizing temporal partition strategies. Experimental evaluations on real world datasets and large synthetic datasets demonstrate the feasibility and scalability of our proposed efficient techniques and optimizations.