46 citations · 114 across the 20 of their papers we have counts for
15 papers · 1 filter
Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions
Rhys Tracy, Haotian Xia, Alex Rasla +2
This research aims to improve the accuracy of complex volleyball predictions and provide more meaningful insights to coaches and players. We introduce a specialized graph encoding…
Link Prediction without Graph Neural Networks
Zexi Huang, Mert Kosan, Arlei Silva +1
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Network…
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Kha-Dinh Luong, Mert Kosan, Arlei Lopes Da Silva +1
Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is p…
Global Counterfactual Explainer for Graph Neural Networks
Mert Kosan, Zexi Huang, Sourav Medya +2
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, ther…
Deep Representations for Time-varying Brain Datasets
Sikun Lin, Shuyun Tang, Scott Grafton +1
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (fu…
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Jiayi Yang, Sourav Medya, Wei Ye
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels…