126 citations · 306 across the 18 of their papers we have counts for
19 papers
Graph Denoising Diffusion for Inverse Protein Folding
Kai Yi, Bingxin Zhou, Yiqing Shen +2
Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protei…
Oversquashing in GNNs through the lens of information contraction and graph expansion
Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang +2
The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for pr…
How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images
Yiqing Shen, Bingxin Zhou, Xinye Xiong +2
Gigapixel medical images provide massive data, both morphological textures and spatial information, to be mined. Due to the large data scale in histology, deep learning methods pla…
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…
Robust Graph Representation Learning for Local Corruption Recovery
Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang +4
The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe…
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…
Weisfeiler and Lehman Go Cellular: CW Networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter +4
Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems…
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…
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Xuebin Zheng, Bingxin Zhou, Yu Guang Wang +1
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analy…
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…
Path Integral Based Convolution and Pooling for Graph Neural Networks
Zheng Ma, Junyu Xuan, Yu Guang Wang +2
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and poolin…
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
Xuebin Zheng, Bingxin Zhou, Ming Li +2
Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In this paper, we propose a framework for graph neu…
Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma +3
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs…
FaVeST: Fast Vector Spherical Harmonic Transforms
Quoc T. Le Gia, Ming Li, Yu Guang Wang
Vector spherical harmonics on the unit sphere of have broad applications in geophysics, quantum mechanics and astrophysics. In the representation of a tangent vector…
Fast Tensor Needlet Transforms for Tangent Vector Fields on the Sphere
Ming Li, Philip Broadbridge, Andriy Olenko +1
This paper constructs a semi-discrete tight frame of tensor needlets associated with a quadrature rule for tangent vector fields on the unit sphere of …
Fast Haar Transforms for Graph Neural Networks
Ming Li, Zheng Ma, Yu Guang Wang +1
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-str…
Tight framelets and fast framelet filter bank transforms on manifolds
Yu Guang Wang, Xiaosheng Zhuang
Tight framelets on a smooth and compact Riemannian manifold provide a tool of multiresolution analysis for data from geosciences, astrophysics, medical sciences, etc.…