1 citations · 1 across the 6 of their papers we have counts for
7 papers
Temporal Motif Signatures for Temporal Graph Neural Networks
Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
Real temporal interaction streams carry predictive structure in short-horizon motif patterns -- repetition, reciprocity, star diversity, triadic flow -- that vanilla temporal graph…
A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
Jing Liu, Maria Grith, Xiaowen Dong +1
This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.…
Data-Driven Graph Filters via Adaptive Spectral Shaping
Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering that learns a reusable baseline spectral kernel and modulates it with a small set of Gaussian fa…
On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
Ning Zhang, Henry Kenlay, Li Zhang +2
Graph convolutional neural networks (GCNNs) have emerged as powerful tools for analyzing graph-structured data, achieving remarkable success across diverse applications. However, t…
Tactical Asset Allocation with Macroeconomic Regime Detection
Daniel Cunha Oliveira, Dylan Sandfelder, André Fujita +2
This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies curren…
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
Isabelle Lorge, Li Zhang, Xiaowen Dong +1
The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change. W…
Spectral Clustering for Directed Graphs via Likelihood Estimation on Stochastic Block Models
Ning Zhang, Xiaowen Dong, Mihai Cucuringu
Graph clustering is a fundamental task in unsupervised learning with broad real-world applications. While spectral clustering methods for undirected graphs are well-established and…