126 citations · 129 across the 3 of their papers we have counts for
11 papers
Embedding Graphs on Grassmann Manifold
Bingxin Zhou, Xuebin Zheng, Yu Guang Wang +2
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural…
Graph Denoising with Framelet Regularizer
Bingxin Zhou, Ruikun Li, Xuebin Zheng +2
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise. Existing research usually focuses on feature smoot…
Anomaly Detection in Dynamic Graphs via Transformer
Yixin Liu, Shirui Pan, Yu Guang Wang +4
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based a…
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang +4
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems. However, graphs alone cannot capture the multi-level int…
How Framelets Enhance Graph Neural Networks
Xuebin Zheng, Bingxin Zhou, Junbin Gao +4
This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We…
Distributed Learning via Filtered Hyperinterpolation on Manifolds
Guido Montúfar, Yu Guang Wang
Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis…