422 citations · 1.5k across the 47 of their papers we have counts for
49 papers
Learning to Learn Financial Networks for Optimising Momentum Strategies
Xingyue Pu, Stefan Zohren, Stephen Roberts +1
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current proc…
Network Momentum across Asset Classes
Xingyue Pu, Stephen Roberts, Xiaowen Dong +1
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic an…
Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB
Valentina Semenova, Dragos Gorduza, William Wildi +2
A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2…
A Markov Random Field model for Hypergraph-based Machine Learning
Bohan Tang, Keyue Jiang, Laura Toni +2
Understanding the data-generating process is essential for building machine learning models that generalise well while ensuring robustness and interpretability. This paper addresse…
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
Chao Zhang, Xingyue Pu, Mihai Cucuringu +1
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks…
On the Impact of Sample Size in Reconstructing Graph Signals
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Reconstructing a signal on a graph from observations on a subset of the vertices is a fundamental problem in the field of graph signal processing. It is often assumed that adding a…
Structure-Aware Robustness Certificates for Graph Classification
Pierre Osselin, Henry Kenlay, Xiaowen Dong
Certifying the robustness of a graph-based machine learning model poses a critical challenge for safety. Current robustness certificates for graph classifiers guarantee output inva…
Graph Classification Gaussian Processes via Spectral Features
Felix L. Opolka, Yin-Cong Zhi, Pietro Liò +1
Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing b…
Bayesian Optimisation of Functions on Graphs
Xingchen Wan, Pierre Osselin, Henry Kenlay +3
The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be a…
DRew: Dynamically Rewired Message Passing with Delay
Benjamin Gutteridge, Xiaowen Dong, Michael Bronstein +1
Message passing neural networks (MPNNs) have been shown to suffer from the phenomenon of over-squashing that causes poor performance for tasks relying on long-range interactions. T…
Understanding stock market instability via graph auto-encoders
Dragos Gorduza, Xiaowen Dong, Stefan Zohren
Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid…
Transductive Kernels for Gaussian Processes on Graphs
Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng +2
Kernels on graphs have had limited options for node-level problems. To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised le…
Learning Hypergraphs From Signals With Dual Smoothness Prior
Bohan Tang, Siheng Chen, Xiaowen Dong
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes…
Unrolled Graph Learning for Multi-Agent Collaboration
Enpei Zhang, Shuo Tang, Xiaowen Dong +2
Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning…
Behavioral changes during the pandemic worsened income diversity of urban encounters
Takahiro Yabe, Bernardo Garcia Bulle Bueno, Xiaowen Dong +2
Diversity of physical encounters and social interactions in urban environments are known to spur economic productivity and innovation in cities, while also to foster social capital…
Learning to Infer Structures of Network Games
Emanuele Rossi, Federico Monti, Yan Leng +2
Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but al…
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…
Local2Global: A distributed approach for scaling representation learning on graphs
Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2
We propose a decentralised "local2global"' approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…
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…
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova +3
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge…
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…
Beltrami Flow and Neural Diffusion on Graphs
Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard +3
We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positiona…
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Felix L. Opolka, Yin-Cong Zhi, Pietro Liò +1
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes. In particular, when the data exhibit varying levels of smoothness on the gra…