250 citations · 937 across the 31 of their papers we have counts for
33 papers
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…
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O'Bray, Karsten Borgwardt
The Transformer architecture has gained growing attention in graph representation learning recently, as it naturally overcomes several limitations of graph neural networks (GNNs) b…
Weisfeiler and Leman go Machine Learning: The Story so far
Christopher Morris, Yaron Lipman, Haggai Maron +5
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful…
The magnitude vector of images
Michael F. Adamer, Edward De Brouwer, Leslie O'Bray +1
The magnitude of a finite metric space has recently emerged as a novel invariant quantity, allowing to measure the effective size of a metric space. Despite encouraging first resul…