A regularization framework for mobile social network analysis. - Xiaowen Dong 001; Pascal Frossard; Pierre Vandergheynst; Nikolai Nefedov (ICASSP, 2011)

Learning of structured graph dictionaries. - Xuan Zhang; Xiaowen Dong 001; Pascal Frossard (ICASSP, 2012)

Structural analysis of network traffic matrix via relaxed principal component pursuit. - Zhe Wang 024; Kai Hu 004; Ke Xu; Baolin Yin; Xiaowen Dong 001 (Comput. Networks, 2012)

Clustering With Multi-Layer Graphs: A Spectral Perspective. - Xiaowen Dong 001; Pascal Frossard; Pierre Vandergheynst; Nikolai Nefedov (IEEE Trans. Signal Process., 2012)

Inference of mobility patterns via Spectral Graph Wavelets. - Xiaowen Dong 001; Antonio Ortega; Pascal Frossard; Pierre Vandergheynst (ICASSP, 2013)

SaferCity: A System for Detecting and Analyzing Incidents from Social Media. - Michele Berlingerio; Francesco Calabrese; Giusy Di Lorenzo; Xiaowen Dong 001; Yiannis Gkoufas; Dimitrios Mavroeidis (ICDM Workshops, 2013)

Learning Graphs from Signal Observations under Smoothness Prior. - Xiaowen Dong 001; Dorina Thanou; Pascal Frossard; Pierre Vandergheynst (CoRR, 2014)

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds. - Xiaowen Dong 001; Pascal Frossard; Pierre Vandergheynst; Nikolai Nefedov (IEEE Trans. Signal Process., 2014)

Laplacian matrix learning for smooth graph signal representation. - Xiaowen Dong 001; Dorina Thanou; Pascal Frossard; Pierre Vandergheynst (ICASSP, 2015)

Multiscale event detection in social media. - Xiaowen Dong 001; Dimitrios Mavroeidis; Francesco Calabrese; Pascal Frossard (Data Min. Knowl. Discov., 2015)

Multi-modal Image Retrieval with Random Walk on Multi-layer Graphs. - Renata Khasanova; Xiaowen Dong 001; Pascal Frossard (ISM, 2016)

Learning Laplacian Matrix in Smooth Graph Signal Representations. - Xiaowen Dong 001; Dorina Thanou; Pascal Frossard; Pierre Vandergheynst (IEEE Trans. Signal Process., 2016)

Learning Heat Diffusion Graphs. - Dorina Thanou; Xiaowen Dong 001; Daniel Kressner; Pascal Frossard (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Entropic Spectral Learning in Large Scale Networks. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2018)

Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey. - Albert Ali Salah; Alex Pentland; Bruno Lepri; Emmanuel Letouzé; Patrick Vinck; Yves-Alexandre de Montjoye; Xiaowen Dong 001; Özge Dagdelen (CoRR, 2018)

Learning Quadratic Games on Networks. - Yan Leng; Xiaowen Dong 001; Alex Pentland (CoRR, 2018)

Methods for quantifying effects of social unrest using credit card transaction data. - Xiaowen Dong 001; Joachim Meyer 002; Erez Shmueli; Burçin Bozkaya; Alex Pentland (EPJ Data Sci., 2018)

Behavioral attributes and financial churn prediction. - Erdem Kaya; Xiaowen Dong 001; Yoshihiko Suhara; Selim Balcisoy; Burçin Bozkaya; Alex 'Sandy' Pentland (EPJ Data Sci., 2018)

Social Bridges in Urban Purchase Behavior. - Xiaowen Dong 001; Yoshihiko Suhara; Burçin Bozkaya; Vivek K. Singh 001; Bruno Lepri; Alex 'Sandy' Pentland (ACM Trans. Intell. Syst. Technol., 2018)

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey. - Albert Ali Salah; Alex Pentland; Bruno Lepri; Emmanuel Letouzé; Yves-Alexandre de Montjoye; Xiaowen Dong 001; Özge Dagdelen; Patrick Vinck (Data for Refugees Challenge, 2019)

Error Analysis on Graph Laplacian Regularized Estimator. - Kaige Yang; Xiaowen Dong 001; Laura Toni (CoRR, 2019)

Segregated interactions in urban and online spaces. - Xiaowen Dong 001; Alfredo Jose Morales; Eaman Jahani; Esteban Moro; Bruno Lepri; Burçin Bozkaya; Carlos Sarraute; Yaneer Bar-Yam; Alex Pentland (CoRR, 2019)

A Maximum Entropy approach to Massive Graph Spectra. - Diego Granziol; Robin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (CoRR, 2019)

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. - Diego Granziol; Bin Xin Ru; Stefan Zohren; Xiaowen Dong 001; Michael A. Osborne; Stephen J. Roberts (Entropy, 2019)

Learning Graphs From Data: A Signal Representation Perspective. - Xiaowen Dong 001; Dorina Thanou; Michael G. Rabbat; Pascal Frossard (IEEE Signal Process. Mag., 2019)

Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis. - Kaige Yang; Laura Toni; Xiaowen Dong 001 (AISTATS, 2020)

Mobility Networks for Predicting Gentrification. - Oliver Gardiner; Xiaowen Dong 001 (COMPLEX NETWORKS (2), 2020)

On The Stability of Polynomial Spectral Graph Filters. - Henry Kenlay; Dorina Thanou; Xiaowen Dong 001 (ICASSP, 2020)

Learning Quadratic Games on Networks. - Yan Leng; Xiaowen Dong 001; Junfeng Wu 001; Alex Pentland (ICML, 2020)

Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel. - Bin Xin Ru; Xingchen Wan; Xiaowen Dong 001; Michael A. Osborne (CoRR, 2020)

Sentiment Diffusion in Financial News Networks and Associated Market Movements. - Xingchen Wan; Jie Yang; Slavi Marinov; Jan-Peter Calliess; Stefan Zohren; Xiaowen Dong 001 (CoRR, 2020)

Segregated interactions in urban and online space. - Xiaowen Dong 001; Alfredo Jose Morales; Eaman Jahani; Esteban Moro; Bruno Lepri; Burçin Bozkaya; Carlos Sarraute; Yaneer Bar-Yam; Alex Pentland (EPJ Data Sci., 2020)

Graph Signal Processing for Machine Learning: A Review and New Perspectives. - Xiaowen Dong 001; Dorina Thanou; Laura Toni; Michael M. Bronstein; Pascal Frossard (IEEE Signal Process. Mag., 2020)

On The Stability of Graph Convolutional Neural Networks Under Edge Rewiring. - Henry Kenlay; Dorina Thanou; Xiaowen Dong 001 (ICASSP, 2021)

Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. - Bin Xin Ru; Xingchen Wan; Xiaowen Dong 001; Michael A. Osborne (ICLR, 2021)

Interpretable Stability Bounds for Spectral Graph Filters. - Henry Kenlay; Dorina Thanou; Xiaowen Dong 001 (ICML, 2021)

Beltrami Flow and Neural Diffusion on Graphs. - Ben Chamberlain 001; James Rowbottom; Davide Eynard; Francesco Di Giovanni; Xiaowen Dong 001; Michael M. Bronstein (NeurIPS, 2021)

Learning to Learn Graph Topologies. - Xingyue Pu; Tianyue Cao; Xiaoyun Zhang; Xiaowen Dong 001; Siheng Chen (NeurIPS, 2021)

Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Robin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (NeurIPS, 2021)

Local2Global: Scaling global representation learning on graphs via local training. - Lucas G. S. Jeub; Giovanni Colavizza; Xiaowen Dong 001; Marya Bazzi; Mihai Cucuringu (CoRR, 2021)

Adversarial Attacks on Graph Classification via Bayesian Optimisation. - Xingchen Wan; Henry Kenlay; Binxin Ru; Arno Blaas; Michael A. Osborne; Xiaowen Dong 001 (CoRR, 2021)

Bayesian Topology Learning and noise removal from network data. - Mahmoud Ramezani Mayiami; Mohammad Hajimirsadeghi; Karl Skretting; Xiaowen Dong 001; Rick S. Blum; H. Vincent Poor (Discov. Internet Things, 2021)

Correction to: Bayesian Topology Learning and noise removal from network data. - Mahmoud Ramezani-Mayiami; Mohammad Hajimirsadeghi; Karl Skretting; Xiaowen Dong 001; Rick S. Blum; H. Vincent Poor (Discov. Internet Things, 2021)

Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. - Xingyue Pu; Siu Lun Chau; Xiaowen Dong 001; Dino Sejdinovic (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets. - Felix L. Opolka; Yin-Cong Zhi; Pietro Liò; Xiaowen Dong 001 (AISTATS, 2022)

Understanding over-squashing and bottlenecks on graphs via curvature. - Jake Topping; Francesco Di Giovanni; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2022)

Learning to Infer Structures of Network Games. - Emanuele Rossi; Federico Monti; Yan Leng; Michael M. Bronstein; Xiaowen Dong 001 (ICML, 2022)

On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. - Emanuele Rossi; Henry Kenlay; Maria I. Gorinova 001; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (LoG, 2022)

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity. - Valentin Hofmann; Xiaowen Dong 001; Janet B. Pierrehumbert; Hinrich Schütze (NAACL-HLT (Findings), 2022)

Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. - Diego Granziol; Binxin Ru; Xiaowen Dong 001; Stefan Zohren; Michael A. Osborne; Stephen J. Roberts (Algorithms, 2022)

Unrolled Graph Learning for Multi-Agent Collaboration. - Enpei Zhang; Shuo Tang; Xiaowen Dong 001; Siheng Chen; Yanfeng Wang 001 (CoRR, 2022)

Transductive Kernels for Gaussian Processes on Graphs. - Yin-Cong Zhi; Felix L. Opolka; Yin Cheng Ng; Pietro Liò; Xiaowen Dong 001 (CoRR, 2022)

Learning Hypergraphs From Signals With Dual Smoothness Prior. - Bohan Tang; Siheng Chen; Xiaowen Dong 001 (ICASSP, 2023)

DRew: Dynamically Rewired Message Passing with Delay. - Benjamin Gutteridge; Xiaowen Dong 001; Michael M. Bronstein; Francesco Di Giovanni (ICML, 2023)

Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization. - Haitz Sáez de Ocáriz Borde; Alvaro Arroyo; Ismael Morales; Ingmar Posner; Xiaowen Dong 001 (NeurIPS, 2023)

Bayesian Optimisation of Functions on Graphs. - Xingchen Wan; Pierre Osselin; Henry Kenlay; Binxin Ru; Michael A. Osborne; Xiaowen Dong 001 (NeurIPS, 2023)

Graph classification Gaussian processes via spectral features. - Felix L. Opolka; Yin-Cong Zhi; Pietro Liò; Xiaowen Dong 001 (UAI, 2023)

Structure-aware robustness certificates for graph classification. - Pierre Osselin; Henry Kenlay; Xiaowen Dong 001 (UAI, 2023)

On the Impact of Sample Size in Reconstructing Graph Signals. - Baskaran Sripathmanathan; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2023)

Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects. - Chao Zhang; Xingyue Pu; Mihai Cucuringu; Xiaowen Dong 001 (CoRR, 2023)

Network Momentum across Asset Classes. - Xingyue Pu; Stephen J. Roberts; Xiaowen Dong 001; Stefan Zohren (CoRR, 2023)

Learning to Learn Financial Networks for Optimising Momentum Strategies. - Xingyue Pu; Stefan Zohren; Stephen J. Roberts; Xiaowen Dong 001 (CoRR, 2023)

Hypergraph Structure Inference From Data Under Smoothness Prior. - Bohan Tang; Siheng Chen; Xiaowen Dong 001 (CoRR, 2023)

Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces. - Haitz Sáez de Ocáriz Borde; Alvaro Arroyo; Ismael Morales; Ingmar Posner; Xiaowen Dong 001 (CoRR, 2023)

Gaussian Processes on Graphs Via Spectral Kernel Learning. - Yin-Cong Zhi; Yin Cheng Ng; Xiaowen Dong 001 (IEEE Trans. Signal Inf. Process. over Networks, 2023)

Local2Global: a distributed approach for scaling representation learning on graphs. - Lucas G. S. Jeub; Giovanni Colavizza; Xiaowen Dong 001; Marya Bazzi; Mihai Cucuringu (Mach. Learn., 2023)

STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network. - Isabelle Lorge; Li Zhang 131; Xiaowen Dong 001; Janet B. Pierrehumbert (LREC/COLING, 2024)

Extracting Alpha from Financial Analyst Networks. - Dragos Gorduza; Yaxuan Kong; Xiaowen Dong 001; Stefan Zohren (ICAIF, 2024)

Hypergraph Transformer for Semi-Supervised Classification. - Zexi Liu; Bohan Tang; Ziyuan Ye; Xiaowen Dong 001; Siheng Chen; Yanfeng Wang 001 (ICASSP, 2024)

Hypergraph-Mlp: Learning on Hypergraphs Without Message Passing. - Bohan Tang; Siheng Chen; Xiaowen Dong 001 (ICASSP, 2024)

A Characterization Theorem for Equivariant Networks with Point-wise Activations. - Marco Pacini; Xiaowen Dong 001; Bruno Lepri; Gabriele Santin (ICLR, 2024)

Bayesian Optimization of Functions over Node Subsets in Graphs. - Huidong Liang; Xingchen Wan; Xiaowen Dong 001 (NeurIPS, 2024)

Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching. - Fernando Moreno-Pino; Alvaro Arroyo; Harrison Waldon; Xiaowen Dong 001; Álvaro Cartea (NeurIPS, 2024)

Hypergraph Node Classification With Graph Neural Networks. - Bohan Tang; Zexi Liu; Keyue Jiang; Siheng Chen; Xiaowen Dong 001 (CoRR, 2024)

Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning. - Shuo Tang; Rui Ye 001; Chenxin Xu; Xiaowen Dong 001; Siheng Chen; Yanfeng Wang 001 (CoRR, 2024)

Rough Transformers for Continuous and Efficient Time-Series Modelling. - Fernando Moreno-Pino; Alvaro Arroyo; Harrison Waldon; Xiaowen Dong 001; Álvaro Cartea (CoRR, 2024)

STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network. - Isabelle Lorge; Li Zhang 131; Xiaowen Dong 001; Janet B. Pierrehumbert (CoRR, 2024)

Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering. - Mihai Cucuringu; Xiaowen Dong 001; Ning Zhang (CoRR, 2024)

Bundle Neural Networks for message diffusion on graphs. - Jacob Bamberger; Federico Barbero; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2024)

Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures. - Fernando Moreno-Pino; Alvaro Arroyo; Harrison Waldon; Xiaowen Dong 001; Álvaro Cartea (CoRR, 2024)

A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges. - Yuqi Nie; Yaxuan Kong; Xiaowen Dong 001; John M. Mulvey; H. Vincent Poor; Qingsong Wen; Stefan Zohren (CoRR, 2024)

On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation. - Baskaran Sripathmanathan; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2024)

Graph Classification Gaussian Processes via Hodgelet Spectral Features. - Mathieu Alain; So Takao; Xiaowen Dong 001; Bastian Rieck; Emmanuel Noutahi (CoRR, 2024)

Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation. - Shuo Tang; Xianghe Pang; Zexi Liu; Bohan Tang; Rui Ye 001; Xiaowen Dong 001; Yanfeng Wang 001; Siheng Chen (CoRR, 2024)

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning. - Haitz Sáez de Ocáriz Borde; Artem Lukoianov; Anastasis Kratsios; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2024)

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review. - Rui Ye 001; Xianghe Pang; Jingyi Chai; Jiaao Chen; Zhenfei Yin; Zhen Xiang; Xiaowen Dong 001; Jing Shao; Siheng Chen (CoRR, 2024)

Long-Range Social Influence in Phone Communication Networks on Offline Adoption Decisions. - Yan Leng; Xiaowen Dong 001; Esteban Moro; Alex Pentland (Inf. Syst. Res., 2024)

Graph similarity learning for change-point detection in dynamic networks. - Déborah Sulem; Henry Kenlay; Mihai Cucuringu; Xiaowen Dong 001 (Mach. Learn., 2024)

Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation. - Shuo Tang; Xianghe Pang; Zexi Liu; Bohan Tang; Rui Ye 001; Tian Jin; Xiaowen Dong 001; Yanfeng Wang 001; Siheng Chen (ACL (1), 2025)

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes. - Keyue Jiang; Bohan Tang; Xiaowen Dong 001; Laura Toni (AISTATS, 2025)

Bundle Neural Network for message diffusion on graphs. - Jacob Bamberger; Federico Barbero; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Neural Spacetimes for DAG Representation Learning. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Marc T. Law; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Separation Power of Equivariant Neural Networks. - Marco Pacini; Xiaowen Dong 001; Bruno Lepri; Gabriele Santin (ICLR, 2025)

Training-Free Message Passing for Learning on Hypergraphs. - Bohan Tang; Zexi Liu; Keyue Jiang; Siheng Chen; Xiaowen Dong 001 (ICLR, 2025)

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning. - Alvaro Arroyo; Alessio Gravina; Benjamin Gutteridge; Federico Barbero; Claudio Gallicchio; Xiaowen Dong 001; Michael M. Bronstein; Pierre Vandergheynst (CoRR, 2025)

Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription. - Benjamin Gutteridge; Matthew Thomas Jackson; Toni Kukurin; Xiaowen Dong 001 (CoRR, 2025)

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement. - Huidong Liang; Haitz Sáez de Ocáriz Borde; Baskaran Sripathmanathan; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2025)

GNNs as Predictors of Agentic Workflow Performances. - Yuanshuo Zhang; Yuchen Hou; Bohan Tang; Shuo Chen; Muhan Zhang; Xiaowen Dong 001; Siheng Chen (CoRR, 2025)

Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations. - Mathieu Alain; So Takao; Xiaowen Dong 001; Bastian Rieck; Emmanuel Noutahi (CoRR, 2025)

On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective. - Ning Zhang; Henry Kenlay; Li Zhang 131; Mihai Cucuringu; Xiaowen Dong 001 (CoRR, 2025)

On Measuring Long-Range Interactions in Graph Neural Networks. - Jacob Bamberger; Benjamin Gutteridge; Scott le Roux; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2025)

Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks. - Ali Hariri; Alvaro Arroyo; Alessio Gravina; Moshe Eliasof; Carola-Bibiane Schönlieb; Davide Bacciu; Kamyar Azizzadenesheli; Xiaowen Dong 001; Pierre Vandergheynst (CoRR, 2025)

Bures-Wasserstein Flow Matching for Graph Generation. - Keyue Jiang; Jiahao Cui; Xiaowen Dong 001; Laura Toni (CoRR, 2025)

Natural Evolutionary Search meets Probabilistic Numerics. - Pierre Osselin; Masaki Adachi; Xiaowen Dong 001; Michael A. Osborne (CoRR, 2025)

Understanding stock market instability via graph auto-encoders. - Dragos Gorduza; Stefan Zohren; Xiaowen Dong 001 (EPJ Data Sci., 2025)