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 disjointed 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, significantly reducing the communication overhead and improving incremental graph processing performance. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08X on average (up to 36.66X) in response time.

翻译：现实世界的图不断演化，需要更新先前的分析结果以适应图的变化。通过利用缓存的前一次计算状态，增量图计算可以减少不必要的重新计算。然而，微小的变化可能传播到整个图，导致大规模迭代计算。为解决此问题，本文提出Layph——一个双层图框架。上层是图的骨架，其规模远小于原始图，下层包含若干不相交的子图。Layph将原始图上代价高昂的全局迭代计算限制在小型图骨架及随输入图变化更新的少数子图上。通过这种方式，大量顶点和边不参与迭代计算，显著降低通信开销并提升增量图处理性能。实验结果表明，Layph在响应时间上平均比当前最先进的增量图系统快9.08倍（最高达36.66倍）。