58 citations · 107 across the 33 of their papers we have counts for
34 papers
Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Jintang Li, Yuhong Chen, Ruofan Wu +4
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise mul…
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4
Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such…
SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Neural Networks (GNNs) are exemplary deep models designed for graph data. Message passing mechanism enables GNNs to effectively capture graph topology and push the performanc…
SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
Yuchang Zhu, Jintang Li, Huizhe Zhang +2
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. De…
GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Transformers (GTs), which integrate message passing and self-attention mechanisms simultaneously, have achieved promising empirical results in graph prediction tasks. However…
Are Large Language Models In-Context Graph Learners?
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or…
Measuring Diversity in Synthetic Datasets
Yuchang Zhu, Huizhe Zhang, Bingzhe Wu +5
Large language models (LLMs) are widely adopted to generate synthetic datasets for various natural language processing (NLP) tasks, such as text classification and summarization. H…
Revisiting Graph Autoencoders as Implicit Contrastive Learners
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs, yet they are often studied in isolatio…
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Xinzhou Jin, Jintang Li, Liang Chen +6
Graph neural networks (GNNs) have recently emerged as an effective approach to model neighborhood signals in collaborative filtering. Towards this research line, graph contrastive…
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Yunfei Liu, Jintang Li, Yuehe Chen +9
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning…
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Yuchang Zhu, Jintang Li, Yatao Bian +2
Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sens…
State Space Models on Temporal Graphs: A First-Principles Study
Jintang Li, Ruofan Wu, Xinzhou Jin +3
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors.…
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
Yuchang Zhu, Jintang Li, Zibin Zheng +1
Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and…
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
Huizhe Zhang, Jintang Li, Liang Chen +1
Graph Transformers (GTs) with powerful representation learning ability make a huge success in wide range of graph tasks. However, the costs behind outstanding performances of GTs a…
Rethinking and Simplifying Bootstrapped Graph Latents
Wangbin Sun, Jintang Li, Liang Chen +3
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing m…
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Yuchang Zhu, Jintang Li, Liang Chen +1
Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to…
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Jintang Li, Zheng Wei, Jiawang Dan +9
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literat…
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Jie Liao, Jintang Li, Liang Chen +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the…
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Jintang Li, Wangbin Sun, Ruofan Wu +3
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contra…
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
Spectral Adversarial Training for Robust Graph Neural Network
Jintang Li, Jiaying Peng, Liang Chen +3
Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue…