46 citations · 133 across the 6 of their papers we have counts for
7 papers
Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
Runlin Lei, Lu Yi, Mingguo He +4
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their ro…
Rethinking Link Prediction for Directed Graphs
Mingguo He, Yuhe Guo, Yanping Zheng +3
Link prediction for directed graphs is a crucial task with diverse real-world applications. Recent advances in embedding methods and Graph Neural Networks (GNNs) have shown promisi…
PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer
Jiahong Ma, Mingguo He, Zhewei Wei
Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning. However, many current methods focus on employing shared polynomial coefficie…
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Mingguo He, Zhewei Wei, Shikun Feng +4
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-ba…
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
Mingguo He, Zhewei Wei, Ji-Rong Wen
Designing spectral convolutional networks is a challenging problem in graph learning. ChebNet, one of the early attempts, approximates the spectral graph convolutions using Chebysh…
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Mingguo He, Zhewei Wei, Zengfeng Huang +1
Many representative graph neural networks, e.g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined f…
Approximate Graph Propagation
Hanzhi Wang, Mingguo He, Zhewei Wei +4
Efficient computation of node proximity queries such as transition probabilities, Personalized PageRank, and Katz are of fundamental importance in various graph mining and learning…