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Workshop Sun, Dec 7, 2025 • 8:00 AM – 5:00 PM PST

New Perspectives in Graph Machine Learning

Zhiyang Wang ⋅ Juan Cervino ⋅ Luana Ruiz ⋅ Alejandro Ribeiro ⋅ Stefanie Jegelka ⋅ Charilaos Kanatsoulis
Project Page [ OpenReview] 

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Schedule

Timezone: America/Los_Angeles
8:00 AM
Opening Remarks
Zhiyang Wang ⋅ Juan Cervino
8:05 AM
Keynote 1 -- Yusu Wang
Yusu Wang
8:50 AM
Beyond Sparse Benchmarks: Evaluating GNNs with Realistic Missing Features
Francesco Ferrini ⋅ Veronica Lachi ⋅ Antonio Longa ⋅ Bruno Lepri ⋅ Andrea Passerini ⋅ XIN LIU ⋅ Manfred Jaeger
9:00 AM
Poster Session 1 and Coffee break
Zhiyang Wang
→ Bridging the Divide: End-to-End Sequence–Graph Learning
Yuen Chen ⋅ Yulun Wu ⋅ Samuel Sharpe ⋅ Igor Melnyk ⋅ Nam Nguyen ⋅ Furong Huang ⋅ C. Bayan Bruss ⋅ Rizal Fathony
→ Discovering Transformer Circuits via a Hybrid Attribution and Pruning Framework
Hao Gu ⋅ Vibhas Nair ⋅ Amrithaa Kumar ⋅ Ryan Lagasse ⋅ Sean O'Brien
→ 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 Bronstein ⋅ Xiaowen Dong
→ Learning (Approximately) Equivariant Networks via Constrained Optimization
Andrei Manolache ⋅ Luiz Chamon ⋅ Mathias Niepert
→ Biological Pathway Informed Models with Graph Attention Networks (GAT)
Gavin Wong ⋅ Ping Ho ⋅ Ivan Yeung ⋅ Ka Chun Cheung ⋅ Simon See
→ Topological Clustering of Aphasic Brain Networks
Jiaying Yi ⋅ Jian Yin ⋅ Rahul Ghosal ⋅ Dirk Ouden ⋅ Julius Fridriksson ⋅ Rutvik Desai ⋅ Yuan Wang
→ 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 ⋅ Zhangdie Yuan ⋅ Yuqicheng Zhu ⋅ Xianglong Hu ⋅ Yuan He ⋅ Farimah Poursafaei ⋅ Michael Bronstein ⋅ Andreas Vlachos
→ 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
→ Connected Causal Graphs for Real-World Science
Amine M'Charrak ⋅ Abbavaram Gowtham Reddy ⋅ Thomas Lukasiewicz ⋅ Michael Bronstein ⋅ Krikamol Muandet
→ Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Jonathan Kouchly ⋅ Ben Finkelshtein ⋅ Michael Bronstein ⋅ Ron Levie
→ Re-evaluating the Advancements of Heterophilic Graph Learning
Sitao Luan ⋅ Qincheng Lu ⋅ Chenqing Hua ⋅ Xinyu Wang ⋅ Jiaqi Zhu ⋅ Xiao-Wen Chang
→ Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Forecasting
Fedor Velikonivtsev ⋅ Oleg Platonov ⋅ Gleb Bazhenov ⋅ Mikhail Seleznyov ⋅ Liudmila Prokhorenkova
→ Exploring Heterophily in Graph-level Tasks
Qinhan Hou ⋅ Yilun Zheng ⋅ Xichun Zhang ⋅ Sitao Luan ⋅ Jing Tang
→ FedGraph: A Research Library and Benchmark for Federated Graph Learning
Yuhang Yao ⋅ Yuan Li ⋅ Xinyi (Cynthia) Fan ⋅ Junhao Li ⋅ Kay Liu ⋅ Yu Yang ⋅ Weizhao Jin ⋅ Srivatsan Ravi ⋅ Philip S Yu ⋅ Carlee Joe-Wong
→ Generalizable Insights for Graph Transformers in Theory and Practice
Timo Stoll ⋅ Luis Müller ⋅ Christopher Morris
→ HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
Xingyue Huang ⋅ Michael Galkin ⋅ Michael Bronstein ⋅ Ismail Ilkan Ceylan
→ Long-Range Graph Wavelet Networks
Filippo Guerranti ⋅ Fabrizio Forte ⋅ Simon Markus Geisler ⋅ Stephan Günnemann
→ Posterior Label Smoothing for Node Classification
Jaeseung Heo ⋅ MoonJeong Park ⋅ Dongwoo Kim
→ Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets
Dave Johnson ⋅ Rishabh Anand ⋅ Smita Krishnaswamy ⋅ Michael Perlmutter
→ Predicting Microbial Interactions Using Graph Neural Networks
Elham Gholamzadeh ⋅ Kajal Singla ⋅ Nico Scherf
→ LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
Nagham Osman ⋅ Keyue Jiang ⋅ Davide Buffelli ⋅ Xiaowen Dong ⋅ Laura Toni
→ A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models
Soham Petkar ⋅ Hari K ⋅ Anirudh Vempati ⋅ Akshit Sinha ⋅ Ponnurangam Kumaraguru ⋅ Chirag Agarwal
→ Federated Link Prediction on Dynamic Graphs
Yuhang Yao ⋅ Xinyi (Cynthia) Fan ⋅ Ryan Rossi ⋅ Sungchul Kim ⋅ Handong Zhao ⋅ Tong Yu ⋅ Carlee Joe-Wong
→ Understanding Generalization in Node and Link Prediction
Antonis Vasileiou ⋅ Timo Stoll ⋅ Christopher Morris
→ Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation
Victor Manuel Tenorio Gomez ⋅ Nicolas Zilberstein ⋅ Santiago Segarra ⋅ Antonio G. Marques
→ Second-Order Tensorial Partial Differential Equations on Graphs
Aref Einizade ⋅ Fragkiskos Malliaros ⋅ Jhony H. Giraldo
→ Are Large Language Models Good Temporal Graph Learners?
Shenyang Huang ⋅ Emma Kondrup ⋅ Ali Parviz ⋅ Zachary Yang ⋅ Zifeng Ding ⋅ Michael Bronstein ⋅ Reihaneh Rabbany ⋅ Guillaume Rabusseau
→ Wasserstein Hypergraph Neural Network
Iulia Duta ⋅ Pietro Lió
→ 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 Bronstein ⋅ Reihaneh Rabbany ⋅ Shenyang Huang
9:45 AM
Contributed Talk 2
Carlos A Taveras
9:45 AM
Gromov-Wasserstein Graph Coarsening
Carlos A Taveras ⋅ Santiago Segarra ⋅ Cesar Uribe
9:55 AM
Contributed talk 3
Dhruv Kohli
9:55 AM
Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
Dhruv Kohli ⋅ Sawyer Robertson ⋅ Gal Mishne ⋅ Alex Cloninger
10:10 AM
Keynote 2
Jure Leskovec
10:55 AM
Contributed Talk 4
Songyao Jin
11:05 AM
Contributed Talk 5
Adrian Hayler
11:05 AM
Of Graphs and Tables: Zero-Shot Node Classification with Tabular Foundation Models
Adrian Hayler ⋅ Xingyue Huang ⋅ Ismail Ilkan Ceylan ⋅ Michael Bronstein ⋅ Ben Finkelshtein
11:15 AM
Keynote 3
12:00 PM
Lunch Break
1:30 PM
Keynote 4
Nina Miolane
2:15 PM
Keynote 5
Mathias Niepert
3:00 PM
Poster Session 2 and Coffee break
Zhiyang Wang
→ Rethinking Graph Backdoor Defense: A Topological, Coarse-to-Fine Perspective
jiecheng Zhai ⋅ Xuzeng Li ⋅ Jian Wang ⋅ Jiqiang Liu
→ Unrolled Policy Iteration Via Graph Filters
Sergio Rozada ⋅ Samuel Rey ⋅ Miguel Alcocer Pérez ⋅ Gonzalo Mateos ⋅ Antonio G. Marques
→ WindMiL: Equivariant Graph Learning for Wind Loading Prediction
Themistoklis Vargiemezis ⋅ Charilaos Kanatsoulis ⋅ Catherine Gorle
→ Actions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inference
Ben Finkelshtein ⋅ Silviu Cucerzan ⋅ Sujay Kumar Jauhar ⋅ Ryen White
→ Rademacher Meets Colors: More Expressivity, but at What Cost?
Caio Deberaldini Netto ⋅ Caterina Graziani ⋅ Martin Carrasco ⋅ Ehimare Okoyomon ⋅ Vahan Martirosyan ⋅ Aneeqa Mehrab
→ GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
Francesco Ferrini ⋅ Veronica Lachi ⋅ Antonio Longa ⋅ Bruno Lepri ⋅ Andrea Passerini
→ Inductive Transfer Learning for Graph-Based Recommenders
Florian Grötschla ⋅ Elia Trachsel ⋅ Luca Lanzendörfer ⋅ Roger Wattenhofer
→ Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection
Kartikeya Aneja ⋅ Nagender Aneja ⋅ Murat Kantarcioglu
→ GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
Mayur Choudhary ⋅ Saptarshi Sengupta ⋅ Katerina Potika
→ Uncertainty-Aware Message Passing Neural Networks
Alesia Chernikova ⋅ Moritz Laber ⋅ Narayan Sabhahit ⋅ Tina Eliassi-Rad
→ A Generative Framework for Exchangeable Graphs with Global and Local Latent Structure
Daniele Micheletti ⋅ Federica Zoe Ricci ⋅ Erik Sudderth
→ Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Mohsen Nayebi Kerdabadi ⋅ William A. Byron ⋅ xin sun ⋅ Amirfarrokh Iranitalab
→ AI-Generated Text Detection using ISGraphs and Graph Neural Networks
Andric Valenzuela ⋅ Helena Gomez Adorno ⋅ Manuel Montes
→ RELATE: A Schema-Agnostic Cross-Attention Encoder for Multimodal Relational Graphs
Joe Meyer ⋅ Divyansha Lachi ⋅ Mahmoud Mohammadi ⋅ Roshan Upendra ⋅ Eva L Dyer ⋅ Minghua Li ⋅ Tom Palczewski
→ Galois Theory Challenges Weisfeiler Leman: Invariant Features for Symmetric Matrices and Point Clouds
Caio Deberaldini Netto ⋅ Beatrix Wen ⋅ Thabo Samakhoana ⋅ Teresa Huang ⋅ Soledad Villar
→ Predict Training Data Quality via Its Geometry in Metric Space
Yang Ba ⋅ Mohammad Sadeq Abolhasani ⋅ Rong Pan
→ DAG Convolutional Networks
Hamed Ajorlou ⋅ Samuel Rey ⋅ Gonzalo Mateos
→ Exploiting All Laplacian Eigenvectors for Node Classification with Graph Transformers
Vinam Arora ⋅ Divyansha Lachi ⋅ Shivashriganesh P. Mahato ⋅ Mehdi Azabou ⋅ Zihao Chen ⋅ Eva L Dyer
→ GNN-Parametrized Diffusion Policies for Wireless Resource Allocation
Yigit Berkay Uslu ⋅ Samar Hadou ⋅ Shirin Saeedi Bidokhti ⋅ Alejandro Ribeiro
→ Turning Tabular Foundation Models into Graph Foundation Models
Dmitry Eremeev ⋅ Gleb Bazhenov ⋅ Oleg Platonov ⋅ Artem Babenko ⋅ Liudmila Prokhorenkova
→ Generating Directed Graphs with Dual Attention and Asymmetric Encoding
Alba Carballo Castro ⋅ Manuel Madeira ⋅ Yiming QIN ⋅ Dorina Thanou ⋅ Pascal Frossard
→ Interpretable Regime Trajectories via Generative Graph State-Space Models
Jeremy Baffou ⋅ Adrien Depeursinge ⋅ Dorina Thanou
→ Transferability of Graph Transformers with Convolutional Positional Encodings
Javier Porras-Valenzuela ⋅ Zhiyang Wang ⋅ Xiaotao Shang ⋅ Alejandro Ribeiro
→ Multi-view Graph Condensation via Tensor Decomposition
Nicolas Roque dos Santos ⋅ Dawon Ahn ⋅ Diego Minatel ⋅ Alneu de Andrade Lopes ⋅ Vagelis Papalexakis
→ CR-Graphormer: From Cascades to Tokens via Mesoscopic Graph Rewiring
Meher Chaitanya Pindiprolu ⋅ My Le ⋅ Luana Ruiz
→ Graph Semi-Supervised Learning for Point Classification on Data Manifolds
Caio Deberaldini Netto ⋅ Zhiyang Wang ⋅ Luana Ruiz
→ A scalable platform to build the data layer of knowledge graph AI
Lucas Vittor ⋅ Iñaki Arango ⋅ Ayush Noori ⋅ Joaquin Polonuer ⋅ Marinka Zitnik
→ Efficient and Expressive Graph Neural Networks
Monika Varshney ⋅ Tanima Dutta
→ 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
→ MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
Jesse He ⋅ Helen Jenne ⋅ Max Vargas ⋅ Davis Brown ⋅ Gal Mishne ⋅ Yusu Wang ⋅ Henry Kvinge
→ Diffusion-Generated Social Graphs Enhance Bot Detection
Alec Laprevotte ⋅ Ryan Lin ⋅ Siddhartha Ojha
3:50 PM
Contributed Talk 6
Yifei Wang
3:50 PM
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
Xiaojun Guo ⋅ Ang Li ⋅ Yifei Wang ⋅ Stefanie Jegelka ⋅ Yisen Wang
4:00 PM
Panel Discussion
Xiaowen Dong ⋅ Soledad Villar ⋅ Tom Palczewski