6 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…
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…
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…