论文标题
快速可扩展的复杂网络描述符使用Pagerank和持续的同源性
Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology
论文作者
论文摘要
图形的Pagerank是图表的节点集上定义的标量函数，该函数编码图形的节点中心性信息。在本文中，我们使用Pagerank函数以及持久的同源性来获取可扩展的图形描述符，并利用它比较图之间的相似性。对于给定的图形$ g（v，e）$，我们的描述符可以在$ o（| e |α（| v |））$中计算，其中$α$是倒数的ackermann函数，使其在大量图上可扩展且可计算。我们通过在多个形状网格数据集上利用它来显示我们方法的有效性。
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.
