Efficient Network Representation Learning via Cluster Similarity

Abstract

Network representation learning is a de-facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is $n \times n$, it needs $O(n^3)$ time and $O(n^2)$ space to perform network representation learning. The proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since $l \ll n$, we can efficiently obtain the representations of clusters from a small $l \times l$ similarity matrix. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches.