PageRank and The K-Means Clustering Algorithm

2020/05/10 by Hajij, Mustafa, Said, Eyad, Todd, Robert
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.2005.04774

Abstract

We utilize the PageRank vector to generalize the k-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph. Furthermore, we show how our method can be generalized to metric spaces and apply it to other domains such as point clouds and triangulated meshes

Related

• PageRank Algorithm using Eigenvector Centrality -- New Approach
• Enforcing Katz and PageRank Centrality Measures in Complex Networks
• Axiomatic Characterization of PageRank
• A Modular Framework for Centrality and Clustering in Complex Networks
• Local Clustering on Complex Graphs and Complex Hypergraphs