59 citations · 217 across the 20 of their papers we have counts for
7 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.…
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…
A Formal Approach to Modeling the Cost of Cognitive Control
Kayhan Ozcimder, Biswadip Dey, Sebastian Musslick +4
This paper introduces a formal method to model the level of demand on control when executing cognitive processes. The cost of cognitive control is parsed into an intensity cost whi…
On Sampling from Massive Graph Streams
Nesreen K. Ahmed, Nick Duffield, Theodore Willke +1
We propose Graph Priority Sampling (GPS), a new paradigm for order-based reservoir sampling from massive streams of graph edges. GPS provides a general way to weight edge sampling…