Higher-order Network Representation Learning

Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
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)
more » ... ross a wide variety of networks and embedding methods.
doi:10.1145/3184558.3186900 dblp:conf/www/RossiAK18 fatcat:zav4qhlmv5debjvovpbofkpbom