46 citations · 151 across the 12 of their papers we have counts for
12 papers
Learning Laplacian Positional Encodings for Heterophilous Graphs
Michael Ito, Jiong Zhu, Dexiong Chen +2
In this work, we theoretically demonstrate that current graph positional encodings (PEs) are not beneficial and could potentially hurt performance in tasks involving heterophilous…
Learning Long Range Dependencies on Graphs via Random Walks
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enabl…
Endowing Protein Language Models with Structural Knowledge
Dexiong Chen, Philip Hartout, Paolo Pellizzoni +2
Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understa…
Fisher Information Embedding for Node and Graph Learning
Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning no…
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Dexiong Chen, Bowen Fan, Carlos Oliver +1
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences…
Approximate Network Motif Mining Via Graph Learning
Carlos Oliver, Dexiong Chen, Vincent Mallet +2
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying m…