261 citations · 1.1k across the 20 of their papers we have counts for
5 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…
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.…
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…
Estimation of Graphlet Statistics
Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severe…