73 citations · 205 across the 27 of their papers we have counts for
8 papers · 1 filter
Graph similarity learning for change-point detection in dynamic networks
Deborah Sulem, Henry Kenlay, Mihai Cucuringu +1
Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic n…
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru +3
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majorit…
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain +2
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information…
Learning to Learn Graph Topologies
Xingyue Pu, Tianyue Cao, Xiaoyun Zhang +2
Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumpt…
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Xingyue Pu, Siu Lun Chau, Xiaowen Dong +1
The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an inc…
A Maximum Entropy approach to Massive Graph Spectra
Diego Granziol, Robin Ru, Stefan Zohren +3
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically ch…