10 citations · 14 across the 13 of their papers we have counts for
6 papers · 1 filter
From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere
Anoushka Harit, Zhongtian Sun, Jongmin Yu
We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph st…
RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
Zhongtian Sun, Anoushka Harit
We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs. By modelling evolving i…
GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
Zhongtian Sun, Anoushka Harit, Alexandra Cristea +2
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ i…
Causal Spherical Hypergraph Networks for Modelling Social Uncertainty
Anoushka Harit, Zhongtian Sun
Human social behaviour is governed by complex interactions shaped by uncertainty, causality, and group dynamics. We propose Causal Spherical Hypergraph Networks (Causal-SphHN), a p…
Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning
Zhongtian Sun, Anoushka Harit, Pietro Lio
While the impact of batch size on generalisation is well studied in vision tasks, its causal mechanisms remain underexplored in graph and text domains. We introduce a hypergraph-ba…
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs
Anoushka Harit, Zhongtian Sun, Jongmin Yu +1
In the fast-paced and volatile financial markets, accurately predicting stock movements based on financial news is critical for investors and analysts. Traditional models often str…