51 citations · 140 across the 15 of their papers we have counts for
26 papers
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma +6
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It…
Heterogeneous Graphlets
Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza +4
In this paper, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed gra…
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
Guixiang Ma, Yao Xiao, Theodore L. Willke +3
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neur…
Inferring Individual Level Causal Models from Graph-based Relational Time Series
Ryan Rossi, Somdeb Sarkhel, Nesreen Ahmed
In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. W…
Deep Graph Similarity Learning: A Survey
Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke +1
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, su…
Temporal Network Sampling
Nesreen K. Ahmed, Nick Duffield, Ryan A. Rossi
Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them…