Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology
Michigan State University（密歇根州立大学）；University of South Florida（南佛罗里达大学）
英文摘要：
The PageRank of a graph is a scalar function defined on the node set of the graph which encodes nodes centrality information of the graph. In this article, we use the PageRank function along with persistent homology to obtain a scalable graph descriptor and utilize it to compare the similarities between graphs. For a given graph $G(V,E)$, our descriptor can be computed in $O(|E|α(|V|))$, where $α$ is the inverse Ackermann function which makes it scalable and computable on massive graphs. We show the effectiveness of our method by utilizing it on multiple shape mesh datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
