33.1k citations · 35.3k across the 95 of their papers we have counts for
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Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories
Steven J. Krieg, Nitesh V. Chawla, Keith Feldman
The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care. Inspired by the fl…
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Yihong Ma, Ning Yan, Jiayu Li +2
Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification an…
ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns
Taicheng Guo, Changsheng Ma, Xiuying Chen +6
Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer have typically bee…
Graph Neural Prompting with Large Language Models
Yijun Tian, Huan Song, Zichen Wang +5
Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent l…
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle…
Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health
Jennifer J. Schnur, Nitesh V. Chawla
This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its…