Context Call for papers Program Speakers Panelists Accepted Papers Organizers Area Chairs Contact Return To Top

Context

Graphs serve as a powerful representational framework for machine learning, and their integration has substantially advanced the field. Indeed, extensive studies have pushed forward graph machine learning (GML) in both theory and applications. Recently, new perspectives have been emerging in the machine learning community, including algebraic–topological analyses, foundation models, generative models, and large models in applications. Leveraging these ideas for core graph machine learning holds a lot of promise, including the dual benefit of deeper theoretical insight, new capabilities and more powerful, application-aligned algorithms and models. The aim of this workshop is to explore and connect these new perspectives on GML, and to identify overarching challenges and tools – in terms of theory, methodology, and modeling.

Call for papers

This workshop will receive submissions, talks, and poster sessions on a wide range of topics, perspectives, and ideas including but not limited to:

  • Symmetry, Equivariance, and Group-Theoretic Graph Models
  • Continuous and Differential Geometric Models
  • Topological Machine Learning
  • Graph Diffusion Models and Graph Generative Models
  • Graph foundational models and Graph Augmented LLMs
  • Continuous‑Limit and Infinite‑Width Analysis of Graph Machine Learning
  • Transferability and Generalization Properties of Graph Models
  • Graphs for Science and Graph-Based Simulations
  • Novel Graph Machine Learning Architectures
  • Causality and Directed Acyclic Graph learning
  • Self-supervised and Semi-supervised Graph Machine Learning
  • Quantum Graph Machine Learning

The main text of a submitted paper is limited to 6 content pages, including all figures and tables. We encourage submissions to have between 4 and 6 pages. Additional pages containing references, checklist, and the optional technical appendices do not count as content pages. All submission are double blind, and must use the NeurIPS 2025 author kit available here. The review process will be facilitated via OpenReview. Please make sure every author has an OpenReview account ahead of submission.

As per the NeurIPS workshop guidelines, this workshop is not a venue for work that has been previously published in other conferences on machine learning or related fields.

Accepted papers will be accessible via this website ahead of the workshop. Our workshop is non-archival and there are no formal proceedings.

The submission portal can be found here.

Dates and Deadlines

Date
September 7nd, 2025, 11:59 pm AoE Submission deadline
September 22nd, 2025, 11:59 pm AoE Accept/Reject Notification Date
December 6 or December 7, 2025 Workshop Date

Program

Time Session
08:45–09:00 Opening Remarks: Goals, code of conduct, logistics
09:00–09:45 Keynote 1
09:45–10:15 Contributed Talks: 10 min talk + Q&A
10:15–10:45 Coffee and Posters A
10:45–11:30 Keynote 2
12:00–13:30 Lunch
13:30–14:15 Keynote 3
14:15–15:00 Contributed Talks
15:00–15:30 Coffee and Posters B
15:30–16:15 Keynote 4
16:15–16:45 Panel Discussion: “What’s next for GML?”
16:15–17:00 Keynote 5
17:00–17:45 Mentorship Speed‑Networking
17:45–18:00 Closing Remarks: Awards, next steps, resources

Speakers

Jure Leskovec

Jure Leskovec

Professor of Computer Science at Stanford University.

Yusu Wang

Yusu Wang

Professor in Halιcιoğlu Data Science Institute at University of California, San Diego.

Nina Miolane

Nina Miolane

Assistant Professor of Electrical and Computer Engineering at UC Santa Barbara.

Bryan Perozzi

Bryan Perozzi

Research Scientist at Google Research.

Mathias Niepert

Mathias Niepert

Professor (W3) at the University of Stuttgart and a faculty member of the International Max Planck Research School for Intelligent Systems.

Panelists

Soledad Villar

Soledad Villar

Assistant Professor at the Department of Applied Mathematics & Statistics, and Mathematical Institute for Data Science at Johns Hopkins University.

Nayat Sanchez-Pi

Nayat Sanchez-Pi

Executive Director - CEO Inria Chile Foundation.

Xiaowen Dong

Xiaowen Dong

Associate Professor in the Department of Engineering Science at the University of Oxford.

Tom Palczewski

Tom Palczewski

Principal Researcher, SAP SE.

Accepted Papers

Orals

# Title Authors
72 G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning Xiaojun Guo, Ang Li, Yifei Wang, Stefanie Jegelka, Yisen Wang
87 Of Graphs and Tables: Zero-Shot Node Classification with Tabular Foundation Models Adrian Hayler, Xingyue Huang, Ismail Ilkan Ceylan, Michael M. Bronstein, Ben Finkelshtein
93 Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks Songyao Jin, Biwei Huang
103 Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization Dhruv Kohli, Sawyer Jack Robertson, Gal Mishne, Alex Cloninger
123 Gromov-Wasserstein Graph Coarsening Carlos A Taveras, Santiago Segarra, Cesar A Uribe
125 Beyond Sparse Benchmarks: Evaluating GNNs with Realistic Missing Features Francesco Ferrini, Veronica Lachi, Antonio Longa, Bruno Lepri, Andrea Passerini, Xin Liu, Manfred Jaeger

Posters

# Title Authors
1 EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Tien Dang, Truong-Son Hy
2 Re-evaluating the Advancements of Heterophilic Graph Learning Sitao Luan, Qincheng Lu, Will Hua, Xinyu Wang, Jiaqi Zhu, Xiao-Wen Chang
3 On the (Non) Injectivity of Piecewise Linear Janossy Pooling Ilai Reshef, Nadav Dym
4 Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps Aditya Ravuri, Neil D Lawrence
5 HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs Xingyue Huang, Mikhail Galkin, Michael M. Bronstein, Ismail Ilkan Ceylan
6 Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization Yimeng Min, Carla P Gomes
7 A Graph Talks, But Who’s Listening? Rethinking Evaluations for Graph-Language Models Soham Petkar, Hari Aakash K, Anirudh Vempati, Akshit Sinha, Ponnurangam Kumaraguru, Chirag Agarwal
8 FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification Prajit Sengupta, Islem Rekik
9 Wasserstein Hypergraph Neural Network Iulia Duta, Pietro Lio
10 Federated Link Prediction on Dynamic Graphs Yuhang Yao, Xinyi Fan, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Tong Yu, Carlee Joe-Wong
12 Second-Order Tensorial Partial Differential Equations on Graphs Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
14 Biological Pathway Informed Models with Graph Attention Networks (GAT) Gavin Y. Wong, Ping Shu Ho, Ivan Au Yeung, Ka Chun Cheung, Simon See
15 The GNN as a Low-Pass Filter: A Spectral Perspective on Achieving Stability in Neural PDE Solvers Peilin Rao
16 Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs Leyao Wang, Yu Wang, Bo Ni, Yuying Zhao, Hanyu Wang, Yao Ma, Tyler Derr
17 Are Large Language Models Good Temporal Graph Learners? Shenyang Huang, Ali Parviz, Emma Kondrup, Zachary Yang, Zifeng Ding, Michael M. Bronstein, Reihaneh Rabbany, Guillaume Rabusseau
18 CrediBench : Building Web-Scale Network Datasets for Information Integrity Emma Kondrup, Sebastian Sabry, Hussein Abdallah, Zachary Yang, James Zhou, Kellin Pelrine, Jean-François Godbout, Michael M. Bronstein, Reihaneh Rabbany, Shenyang Huang
20 Discovering Transformer Circuits via a Hybrid Attribution and Pruning Framework Hao Gu, Vibhas Nair, Amrithaa Ashok Kumar, Ryan Lagasse, Sean O’Brien
21 LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening Nagham Osman, Keyue Jiang, Davide Buffelli, Xiaowen Dong, Laura Toni
23 Efficient Learning on Large Graphs using a Densifying Regularity Lemma Jonathan Kouchly, Ben Finkelshtein, Michael M. Bronstein, Ron Levie
25 Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning Zifeng Ding, Shenyang Huang, Zeyu Cao, Emma Kondrup, Zachary Yang, Xingyue Huang, Yuan Sui, Moy Yuan, Yuqicheng Zhu, Xianglong Hu, Yuan He, Farimah Poursafaei, Michael M. Bronstein, Andreas Vlachos
26 Long-Range Graph Wavelet Networks Filippo Guerranti, Fabrizio Forte, Simon Geisler, Stephan Günnemann
27 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
28 Predicting Microbial Interactions Using Graph Neural Networks Elham Gholamzadeh, Kajal Singla, Nico Scherf
30 Learning (Approximately) Equivariant Networks via Constrained Optimization Andrei Manolache, Luiz F. O. Chamon, Mathias Niepert
31 Learning the Neighborhood: Contrast-Free Self-Supervised Molecular Graph Pretraining Boshra Ariguib, Mathias Niepert, Andrei Manolache
32 Deep Modularity Networks with Diversity-Preserving Regularization Yasmin Salehi, Dennis Giannacopoulos
33 Generalizable Insights for Graph Transformers in Theory and Practice Timo Stoll, Luis Müller, Christopher Morris
34 Bridging the Divide: End-to-End Sequence–Graph Learning Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony
37 Topological Clustering of Aphasic Brain Networks Jiaying Yi, Jian Yin, Rahul Ghosal, Dirk B. den Ouden, Julius Fridriksson, Rutvik Desai, Yuan Wang
38 Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations Abdullah Alchihabi, Hao Yan, Yuhong Guo
39 FedGraph: A Research Library and Benchmark for Federated Graph Learning Yuhang Yao, Yuan Li, Xinyi Fan, Junhao Li, Kay Liu, Yu Yang, Weizhao Jin, Srivatsan Ravi, Philip S. Yu, Carlee Joe-Wong
40 Understanding Generalization in Node and Link Prediction Antonis Vasileiou, Timo Stoll, Christopher Morris
42 Posterior Label Smoothing for Node Classification Jaeseung Heo, MoonJeong Park, Dongwoo Kim
44 LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks Sowon Jeong, Claire Donnat
46 A New Perspective for Graph Learning Architecture Design: Linearize Your Depth Away Joël Mathys, Roger Wattenhofer
47 Exploring Heterophily in Graph-level Tasks Qinhan Hou, Yilun Zheng, Xichun Zhang, Sitao Luan, Jing Tang
48 Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs My Le, Luana Ruiz, Souvik Dhara
49 Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets David R Johnson, Rishabh Anand, Smita Krishnaswamy, Michael Perlmutter
51 Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs Alberto Cattaneo, Carlo Luschi, Daniel Justus
52 The Cartesian Gaussian Additive Noise Model for Causal Inference with Dependent Samples Bailey Andrew, David Robert Westhead, Luisa Cutillo
53 Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra, Antonio G. Marques
54 Connected Causal Graphs for Real-World Science Amine M’Charrak, Abbavaram Gowtham Reddy, Thomas Lukasiewicz, Michael M. Bronstein, Krikamol Muandet
55 Temporal Graph AutoEncoder: Mapping Dynamic Graphs to Dynamical Systems with Neural ODEs Raphael Romero, Rembert Daems, Tijl De Bie
56 CR-Graphormer: From Cascades to Tokens via Mesoscopic Graph Rewiring Meher Chaitanya, My Le, Luana Ruiz
57 MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning Jesse He, Helen Jenne, Max Vargas, Davis Brown, Gal Mishne, Yusu Wang, Henry Kvinge
58 RELATE: A Schema-Agnostic Cross-Attention Encoder for Multimodal Relational Graphs Joe Meyer, Divyansha Lachi, Mahmoud Mohammadi, Roshan Reddy Upendra, Eva L Dyer, Minghua Li, Tom Palczewski
59 Rademacher Meets Colors: More Expressivity, but at What Cost? Martin Carrasco, Caio Netto, Vahan A. Martirosyan, Aneeqa Mehrab, Ehimare Okoyomon, Caterina Graziani
60 KAN-GCN: Combining Kolmogorov–Arnold Network with Graph Convolution Network for an Accurate Ice Sheet Emulator Zesheng Liu, Younghyun Koo, Maryam Rahnemoonfar
62 Model Extraction Without Graphs Structure: How Homophily Drives Attack Effectiveness Xuehai Wu, Qiong Wu
63 Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees Xiaoyu Li, GUANGYU TANG, Jiaojiao Jiang
65 Inductive Transfer Learning for Graph-Based Recommenders Florian Grötschla, Elia Trachsel, Luca A Lanzendörfer, Roger Wattenhofer
66 Multi-view Graph Condensation via Tensor Decomposition Nícolas Roque dos Santos, Dawon Ahn, Diego Minatel, Alneu de Andrade Lopes, Evangelos E. Papalexakis
68 Diffusion-Generated Social Graphs Enhance Bot Detection Alec Laprevotte, Ryan Y. Lin, Siddhartha Ojha
69 Semantic Priors for Drug–Drug Interaction Prediction Using Compact Graph Encoders Annika Viswesh
71 Actions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inference Ben Finkelshtein, Silviu Cucerzan, Sujay Kumar Jauhar, Ryen W White
75 Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Forecasting Fedor Velikonivtsev, Oleg Platonov, Gleb Bazhenov, Mikhail Seleznyov, Liudmila Prokhorenkova
76 Diffusion-augmented Graph Contrastive Learning for Collaborative Filtering Fan Huang, Jianxiang Yu, Wei Wang
80 Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection Kartikeya Aneja, Nagender Aneja, Murat Kantarcioglu
81 Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection Mohsen Nayebi Kerdabadi, William A. Byron, xin sun, Amirfarrokh Iranitalab
83 Rethinking Graph Backdoor Defense: A Topological, Coarse-to-Fine Perspective jiecheng Zhai, Xuzeng Li, Jian Wang, Jiqiang Liu
85 Turning Tabular Foundation Models into Graph Foundation Models Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov, Artem Babenko, Liudmila Prokhorenkova
86 Foundations for Robust yet Simple Sparse Hierarchical Pooling: A New Perspective on Sparse Graph Pooling Sarith Imaduwage
88 Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics Lovro Rabuzin, Michel Tarnow, Valentina Boeva
89 GNN-Parametrized Diffusion Policies for Wireless Resource Allocation Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti, Alejandro Ribeiro
90 Predict Training Data Quality via Its Geometry in Metric Space Yang Ba, Mohammad Sadeq Abolhasani, Rong Pan
91 Unrolled Policy Iteration Via Graph Filters Sergio Rozada, Samuel Rey, Miguel Alcocer Pérez, Gonzalo Mateos, Antonio G. Marques
92 Transferability of Graph Transformers with Convolutional Positional Encodings Javier Porras-Valenzuela, Zhiyang Wang, Xiaotao Shang, Alejandro Ribeiro
97 AI-Generated Text Detection using ISGraphs and Graph Neural Networks Andric Valdez Valenzuela, Helena Gomez Adorno, Manuel Montes
102 DAG Convolutional Networks Hamed Ajorlou, Samuel Rey, Gonzalo Mateos
104 Galois Theory Challenges Weisfeiler Leman: Invariant Features for Symmetric Matrices and Point Clouds Beatrix Yaxin Wen, Caio Netto, Thabo Samakhoana, Soledad Villar, Ningyuan Huang
105 A scalable platform to build the data layer of knowledge graph AI Lucas Vittor, Iñaki Arango, Ayush Noori, Joaquin Polonuer, Marinka Zitnik
109 Uncertainty-Aware Message Passing Neural Networks Alesia Chernikova, Moritz Laber, Narayan G. Sabhahit, Tina Eliassi-Rad
110 Exploiting All Laplacian Eigenvectors for Node Classification with Graph Transformers Vinam Arora, Divyansha Lachi, Shivashriganesh P. Mahato, Mehdi Azabou, Zihao Chen, Eva L Dyer
111 Graph Semi-Supervised Learning for Point Classification on Data Manifolds Caio Netto, Zhiyang Wang, Luana Ruiz
112 Graph Neural Differential Equations in the Infinite‑Node Limit: Convergence and Rates via Graphon Theory Mingsong Yan, Charles Kulick, Sui Tang
113 Staleness-based Subgraph Sampling for Training GNNs on Large-Scale Graphs Limei Wang, Si Zhang, Hanqing Zeng, Hao Wu, Zhigang Hua, Kaveh Hassani, Andrey Malevich, Bo Long, Shuiwang Ji
116 GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification Mayur Choudhary, Saptarshi Sengupta, Katerina Potika
117 Efficient and Expressive Graph Neural Networks Monika Varshney, Tanima Dutta
118 Generating Directed Graphs with Dual Attention and Asymmetric Encoding Alba Carballo-Castro, Manuel Madeira, Yiming QIN, Dorina Thanou, Pascal Frossard
119 Interpretable Regime Trajectories via Generative Graph State-Space Models Jeremy Baffou, Adrien Depeursinge, Dorina Thanou
120 GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Francesco Ferrini, Veronica Lachi, Antonio Longa, Bruno Lepri, Andrea Passerini
121 WindMiL: Equivariant Graph Learning for Wind Loading Prediction Themistoklis Vargiemezis, Charilaos I. Kanatsoulis, Catherine Gorle
122 Graph Representation Learning with Diffusion Generative Models Daniel Wesego
124 A Generative Framework for Exchangeable Graphs with Global and Local Latent Structure Daniele Micheletti, Federica Zoe Ricci, Erik B. Sudderth
126 Laplacian-Guided Denoising Graph Diffusion for Graph Learning with an Adaptive Prior Seoyoon Kim, Hyemin Jung, Woohyung Lim
128 Nonlinear Laplacians Improve Signed-Directed Graph Learning Ali Parviz, Yuichi Yoshida
129 When Curvature Beats Dimension: Euclidean Limits and Hyperbolic Design Rules for Trees Sarwesh Rauniyar

Organizers

Juan Cervino

Juan Cervino

Postdoctoral Researcher at Massachusetts Institute of Technology.

Stefanie Jegelka

Stefanie Jegelka

Associate Professor (on leave) at MIT EECS, and a Humboldt Professor at TU Munich.

Charilaos Kanatsoulis

Charilaos Kanatsoulis

Research Associate in the Department of Computer Science at Stanford University.

Alejandro Ribeiro

Alejandro Ribeiro

Professor of Electrical and Systems Engineering at the University of Pennsylvania.

Luana Ruiz

Luana Ruiz

Assistant Professor, Department of Applied Mathematics and Statistics, Johns Hopkins University

Zhiyang Wang

Zhiyang Wang

Postdoc Scholar in Halıcıoğlu Data Science Institute at UCSD.

Area Chairs

Name Institution
Michael A. Perlmutter Assistant Professor at Boise State University
Venkata S.S. Gandikota Assistant Professor at Syracuse University
Gonzalo Mateos Professor at University of Rochester
Ellen Vitercik Assistant Professor at Stanford University
Kaixiong Zhou Assistant Professor at North Carolina State University
Jundong Li Assistant Professor at University of Virginia
Michael Galkin Senior Research Scientist at Google Research
Jhony Giraldo Assistant Professor at Institut Polytechnique de Paris
Alex Tong Assistant Professor at Duke University
Yuning You Postdoc at California Institute of Technology
Siheng Chen Associate Professor at Shanghai Jiao Tong University
Melanie Weber Assistant Professor at Harvard University