48 citations · 110 across the 7 of their papers we have counts for
7 papers
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Adam Foster, Árpi Vezér, Craig A Glastonbury +3
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including…
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil, Joss Briody, Alix Lacoste +3
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowled…
An Algebraic Theory of Complexity for Discrete Optimisation
David A. Cohen, Martin C. Cooper, Paidi Creed +2
Discrete optimisation problems arise in many different areas and are studied under many different names. In many such problems the quantity to be optimised can be expressed as a su…
The number of Euler tours of a random directed graph
Páidí Creed, Mary Cryan
In this paper we obtain the expectation and variance of the number of Euler tours of a random Eulerian directed graph with fixed out-degree sequence. We use this to obtain the asym…
The tractability of CSP classes defined by forbidden patterns
David A. Cohen, Martin C. Cooper, Páidí Creed +1
The constraint satisfaction problem (CSP) is a general problem central to computer science and artificial intelligence. Although the CSP is NP-hard in general, considerable effort…