222 citations · 768 across the 61 of their papers we have counts for
8 papers · 1 filter
Inductive Representation Learning in Large Attributed Graphs
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou +4
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at…
Similarity-based Multi-label Learning
Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry +1
Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called…
Deep Graph Attention Model
John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph feat…
A Framework for Generalizing Graph-based Representation Learning Methods
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou +4
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g.…
Network Classification and Categorization
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff +5
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from…
Deep Feature Learning for Graphs
Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, D…