Relational Similarity Machines (RSM): A Similarity-based Learning Framework for Graphs

IEEE BigData

Publication date: December 10, 2018

Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed, Hoda Eldardiry

Relational machine learning has become increasingly important due to the recent proliferation and ubiquity of network data. However, existing methods are not designed for interactive learning and have many unrealistic assumptions that greatly limit their utility in practice. For instance, most existing work has focused on graphs with high relational autocorrelation (homophily) and perform poorly otherwise. To overcome these limitations, this paper presents a similarity-based relational learning framework called Relational Similarity Machines (RSM) for networks with arbitrary relational autocorrelation. The RSM framework is designed to be fast, accurate, and flexible for learning on a wide variety of networks. The experiments demonstrate the effectiveness of the RSM framework.

Research Area:  Adobe Research iconAI & Machine Learning