43 citations · 98 across the 11 of their papers we have counts for
11 papers
Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction
Zongyue Qin, Shichang Zhang, Mingxuan Ju +3
Link prediction is a crucial graph-learning task with applications including citation prediction and product recommendation. Distilling Graph Neural Networks (GNNs) teachers into M…
How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
Hongzhe Du, Weikai Li, Min Cai +5
Post-training is essential for the success of large language models (LLMs), transforming pre-trained base models into more useful and aligned post-trained models. While plenty of w…
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Shichang Zhang, Da Zheng, Jiani Zhang +6
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machi…
Automated Molecular Concept Generation and Labeling with Large Language Models
Zimin Zhang, Qianli Wu, Botao Xia +4
Artificial intelligence (AI) is transforming scientific research, with explainable AI methods like concept-based models (CMs) showing promise for new discoveries. However, in molec…
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Qi Zhu, Da Zheng, Xiang Song +4
Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) hav…
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Haoyu Li, Shichang Zhang, Longwen Tang +2
Metallic Glasses (MGs) are widely used materials that are stronger than steel while being shapeable as plastic. While understanding the structure-property relationship of MGs remai…
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Xiaoxuan Wang, Ziniu Hu, Pan Lu +7
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebra…
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan +7
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability…
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Shichang Zhang, Yozen Liu, Neil Shah +1
Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to c…
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Shichang Zhang, Yozen Liu, Yizhou Sun +1
Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deploym…
Motif-Driven Contrastive Learning of Graph Representations
Shichang Zhang, Ziniu Hu, Arjun Subramonian +1
Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive…