109 citations · 196 across the 30 of their papers we have counts for
32 papers
A Scalable Method for Readable Tree Layouts
Kathryn Gray, Mingwei Li, Reyan Ahmed +4
Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or…
Extreme-scale many-against-many protein similarity search
Oguz Selvitopi, Saliya Ekanayake, Giulia Guidi +7
Similarity search is one of the most fundamental computations that are regularly performed on ever-increasing protein datasets. Scalability is of paramount importance for uncoverin…
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy
Md. Khaledur Rahman, Ariful Azad
Graph Neural Networks (GNNs) are widely used to perform different machine learning tasks on graphs. As the size of the graphs grows, and the GNNs get deeper, training and inference…
Graphical Games for UAV Swarm Control Under Time-Varying Communication Networks
Malintha Fernando, Ransalu Senanayake, Ariful Azad +1
We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV) swarms operating under time-varying communication networks. Our framework builds on the concept of gra…
MarkovGNN: Graph Neural Networks on Markov Diffusion
Md. Khaledur Rahman, Abhigya Agrawal, Ariful Azad
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovG…
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
Md. Khaledur Rahman, Ariful Azad
Graph representation learning is a fast-growing field where one of the main objectives is to generate meaningful representations of graphs in lower-dimensional spaces. The learned…