OPTANE

Abstract

Networks provide a powerful representation tool for modeling dyadic interactions among interconnected entities in a complex system. For many applications such as social network analysis, it is common for the entities to appear in more than one network. Network alignment (NA) is an important first step towards learning the entities' behavior across multiple networks by finding the correspondence between similar nodes in different networks. However, learning the proper alignment matrix in noisy networks is a challenge due to the difficulty in preserving both the neighborhood topology and feature consistency of the aligned nodes. In this paper, we present OPTANE, a robust unsupervised network alignment framework, inspired from an optimal transport theory perspective. The framework provides a principled way to combine node similarity with topology information to learn the alignment matrix. Experimental results conducted on both synthetic and real-world data attest to the effectiveness of the OPTANE framework compared to other baseline approaches.