Towards PageRank Update in a Streaming Graph by Incremental Random Walk

Abstract

As the internet and the Internet of Things (IoT) have been widely applied in many application fields, a large number of graphs are continuously produced and change over time, which leads to difficulties in graph analysis and utilization. This paper studies a PageRank update algorithm for a streaming graph using incremental random walk. We focus on the information about the local changes of nodes and edges in the current graph, analyze the impact of such local changes on this current graph, and then use the idea behind wave propagation theory to seek and determine all affected nodes that need to be updated their PageRank in the new graph. For new nodes, the existing nodes in the current graph that are connected with these new nodes are aggregated into a supernode, and the PageRank of the new nodes is solved in the new graph with the supernode. Finally, we conduct a series of experiments on real-world and synthetic graph datasets. Compared with the state-of-the-art incremental computing algorithm, our algorithm not only ensures the accuracy of calculating the PageRank in a large streaming graph, but also speeds up the computational process by avoiding many redundant computations.