74 citations · 256 across the 17 of their papers we have counts for
17 papers
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation
Shuang Wang, Bahaeddin Eravci, Rustam Guliyev +1
Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of e…
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Gunduz Vehbi Demirci, Aparajita Haldar, Hakan Ferhatosmanoglu
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms…
RAGUEL: Recourse-Aware Group Unfairness Elimination
Aparajita Haldar, Teddy Cunningham, Hakan Ferhatosmanoglu
While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are…
GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy
Teddy Cunningham, Konstantin Klemmer, Hongkai Wen +1
Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads…
Temporal Cascade Model for Analyzing Spread in Evolving Networks with Disease Monitoring Applications
Aparajita Haldar, Shuang Wang, Gunduz Demirci +2
Current approaches for modeling propagation in networks (e.g., spread of disease) are unable to adequately capture temporal properties of the data such as order and duration of evo…
Collective Shortest Paths for Minimizing Congestion on Temporal Load-Aware Road Networks
Chris Conlan, Teddy Cunningham, Gunduz Vehbi Demirci +1
Shortest path queries over graphs are usually considered as isolated tasks, where the goal is to return the shortest path for each individual query. In practice, however, such quer…