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Xiaowen Dong

7 papers hereh-index 217 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author3

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL1
  • q-fin.PM1
  • stat.ML1
same name
  • Xiaowen Dong — 49 papers, h 29
  • Xiaowen Dong — 12 papers, h 6
  • Xiaowen Dong — 9 papers, h 7
  • Xiaowen Dong — 8 papers, h 5
  • Xiaowen Dong — 4 papers, h 1
  • Xiaowen Dong — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedSpectral Clustering for Directed Graphs via Likelihood Estimation on Stochastic Block Models

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

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…

cs.LG2026

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.…

cs.LG2026

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…

cs.LG2025

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…

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