17 citations · 41 across the 11 of their papers we have counts for
12 papers
ReMath: Benchmarking Theorem Retrieval in Research-Level Mathematics
Zicheng Lyu, Wenjie Yang, Shengzhong Zhang +1
Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the literature. When a proof rea…
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
Yuxuan Xiao, Shengzhong Zhang
Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interactions and inter-label depende…
Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning
Jiaxing Guo, Wenjie Yang, Shengzhong Zhang +4
Outcome-rewarded Large Language Models (LLMs) have demonstrated remarkable success in mathematical problem-solving. However, this success often masks a critical issue: models frequ…
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning
Wenjie Yang, Shengzhong Zhang, Jiaxing Guo +1
Graph recommender (GR) is a type of graph neural network (GNNs) encoder that is customized for extracting information from the user-item interaction graph. Due to its strong perfor…
Understanding Community Bias Amplification in Graph Representation Learning
Shengzhong Zhang, Wenjie Yang, Yimin Zhang +3
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different clas…
StructComp: Substituting Propagation with Structural Compression in Training Graph Contrastive Learning
Shengzhong Zhang, Wenjie Yang, Xinyuan Cao +2
Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effe…
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Liang Yan, Gengchen Wei, Chen Yang +2
This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanc…
Geometric Imbalance in Semi-Supervised Node Classification
Liang Yan, Shengzhong Zhang, Bisheng Li +6
Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the…
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Bisheng Li, Min Zhou, Shengzhong Zhang +3
Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph c…
Scaling Up Graph Neural Networks Via Graph Coarsening
Zengfeng Huang, Shengzhong Zhang, Chong Xi +2
Scalability of graph neural networks remains one of the major challenges in graph machine learning. Since the representation of a node is computed by recursively aggregating and tr…
SCE: Scalable Network Embedding from Sparsest Cut
Shengzhong Zhang, Zengfeng Huang, Haicang Zhou +1
Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the unde…
Effective Stabilized Self-Training on Few-Labeled Graph Data
Ziang Zhou, Jieming Shi, Shengzhong Zhang +2
Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs where only a subset of nodes have class labels. However, under extreme cases when very f…