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

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph which is much smaller than the original graph, and the lower layer has some disjoint subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, which significantly reduces the computation overhead and improves the performance of incremental graph processing. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08× on average (up to 36.66×) in response time.