21 citations · 63 across the 35 of their papers we have counts for
8 papers · 1 filter
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le +9
Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged…
Training MLPs on Graphs without Supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges…
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and s…
Can LLMs Convert Graphs to Text-Attributed Graphs?
Zehong Wang, Sidney Liu, Zheyuan Zhang +3
Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data…
GFT: Graph Foundation Model with Transferable Tree Vocabulary
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla +2
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Gr…
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
Zheyuan Zhang, Zehong Wang, Shifu Hou +7
The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective trea…
SimMLP: Training MLPs on Graphs without Supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges…
Subgraph Pooling: Tackling Negative Transfer on Graphs
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead…