Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology
Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology |
Abstract
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 $O(|E|alpha(|V|))$, where $alpha$ 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.
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Citation
Mustafa Hajij, Paul Rosen, and Elizabeth Munch. Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology. International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 2020.
Bibtex
@inproceedings{hajij2020fast, title = {Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology}, author = {Hajij, Mustafa and Rosen, Paul and Munch, Elizabeth}, booktitle = {International Conference on Intelligent Data Science Technologies and Applications (IDSTA)}, year = {2020}, abstract = {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 $O(|E|alpha(|V|))$, where $alpha$ 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.} }