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Real-world graphs are constantly evolving, which demands updates of theprevious analysis results to accommodate graph changes. By using the memoizedprevious computation state, incremental graph computation can reduceunnecessary recomputation. However, a small change may propagate over the wholegraph and lead to large-scale iterative computations. To address this problem,we propose Layph, a two-layered graph framework. The upper layer is a skeletonof the graph, which is much smaller than the original graph, and the lowerlayer has some disjointed subgraphs. Layph limits costly global iterativecomputations on the original graph to the small graph skeleton and a fewsubgraphs updated with the input graph changes. In this way, many vertices andedges are not involved in iterative computations, significantly reducing thecommunication overhead and improving incremental graph processing performance.Our experimental results show that Layph outperforms current state-of-the-artincremental graph systems by 9.08X on average (up to 36.66X) in response time.

Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph