Kairos: Efficient Temporal Graph Analytics on a Single Machine

Many important societal problems are naturally modeled as algorithms over temporal graphs. To date, however, most graph processing systems remain inefficient as they rely on distributed processing even for graphs that fit well within a commodity server's available storage. In this paper, we introduce Kairos, a temporal graph analytics system that provides application developers a framework for efficiently implementing and executing algorithms over temporal graphs on a single machine. Specifically, Kairos relies on fork-join parallelism and a highly optimized parallel data structure as core primitives to maximize performance of graph processing tasks needed for temporal graph analytics. Furthermore, we introduce the notion of selective indexing and show how it can be used with an efficient index to speedup temporal queries. Our experiments on a 24-core server show that our algorithms obtain good parallel speedups, and are significantly faster than equivalent algorithms in existing temporal graph processing systems: up to 60x against a shared-memory approach, and several orders of magnitude when compared with distributed processing of graphs that fit within a single server.