most citedInductive Representation Learning in Large Attributed Graphs

21 citations · 50 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.HE2020

The observation of the Crab Nebula with LHAASO-KM2A for the performance study

F. Aharonian, Q. An, Axikegu +246

As a sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A is mainly designed to cover a large fraction of the northern sky to hunt for gamma-ray sources at en…

stat.ML201721 cited

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…

stat.ML2017

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…

stat.ML201716 cited

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.…

cs.SI201713 cited

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…