222 citations · 762 across the 46 of their papers we have counts for
4 papers · 1 filter
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
Guixiang Ma, Nesreen K. Ahmed, Ted Willke +4
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic dist…
Predicting Graph Categories from Structural Properties
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff +5
This paper has been withdrawn from arXiv.org due to a disagreement among the authors related to several peer-review comments received prior to submission on arXiv.org. Even though…
Learning Role-based Graph Embeddings
Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee +4
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features…
HONE: Higher-Order Network Embeddings
Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh +3
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 fl…