250 citations · 937 across the 31 of their papers we have counts for
19 papers · 1 filter
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…
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…
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennet, Drago Plecko +5
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathoge…
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
Leslie O'Bray, Max Horn, Bastian Rieck +1
Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a princ…
Topological Graph Neural Networks
Max Horn, Edward De Brouwer, Michael Moor +3
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present…