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Recently, there have been many disk-based systems proposed for iterative graph processing. In the popular vertex/edge-centric systems, an iterative directed graph algorithm needs to reprocess many partitions so as to update their vertices’ states according to other non-convergent vertices for the unawareness of their dependencies. As a result, it induces high data access cost and a long time to converge. To tackle this problem, we propose a novel system for iterative directed graph processing with taking advantage of the strongly connected component (SCC) structure. It stores a directed graph into a directed acyclic graph (DAG) sketch, with each node representing a SCC in the original data graph. During execution, the SCCs are loaded into memory for processing in a parallel way according to the topological order of the DAG sketch, and the vertices in each SCC are tried to be handled along the directed paths. In this way, each SCC is able to reach convergence in order and needs to be loaded into the main memory for exactly once, getting much lower data access cost and faster convergence. Besides, the vertices of each SCC need fewer updates for convergence. We further develop a lightweight approach to maintain the DAG sketch and handle SCCs in an incremental way for evolving graphs. Compared with the state-of-the-art methods, experimental results show that our approach achieves a performance improvements of 1.46-8.37 times for static graphs, and can reduce the execution time by 61.4-72.7 percent for evolving graphs. [ABSTRACT FROM AUTHOR]

For large-scale graph analysis on a single PC, asynchronous processing methods are known to converge more quickly than the synchronous approach, because of more efficient propagation of vertices state. However, current asynchronous methods are still very suboptimal in propagating state across different graph partitions. This presents a bottleneck for cross-partition state update and slows down the convergence of the processing task. To tackle this problem, we propose a new method, named the HotGraph, to faster graph processing by extracting a backbone structure, called hot graph, that spans all the partitions of the original graph. With this approach, most cross-partition state propagations in traditional solutions now take place within only a few hot graph partitions, thus removing the cross-partition bottleneck. We also develop a partition scheduling algorithm to maximize the hot graph’s effectiveness by keeping it in memory and assigning it the highest priority for processing as much as possible. A forward and backward sweeping execution strategy is then proposed to further accelerate the convergence. Experimental results show that HotGraph can reduce the number of vertex state updates processed by 51.5 percent, compared with state-of-the-art schemes. Applying our optimizations further reduces this number by 72.6 percent and the execution time by 80.8 percent. [ABSTRACT FROM AUTHOR]