Publications

Higher-Order Network Representation Learning

Proceedings of the 27th International Conference Companion on World Wide Web (WWW)

Published April 27, 2018

Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations. In all cases, HONE outperforms recent embedding methods that are unable to capture higher-order structures with a mean relative gain in AUC of 19% (and up to 75% gain) across a wide variety of networks and embedding methods.


Research Area:  AI & Machine Learning