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arXivU Leeds U Science and Technology of Chin South China Normal Tsinghua Chinese University of Hong Kong Graph neural networks

SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection

SAHG detects LLM-driven social bots by applying direction-dependent hyperbolic curvature and dual-channel feature fusion, achieving top accuracy and F1 across three benchmarks.

Hanning Lu, Yingguang Yang, Jinwei Su, Yang; Liu and 7 more

Published May 28, 2026 · 0 citations

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AAAI 2025U Virginia Graph neural networks

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning

FedVN uses learnable virtual nodes and client-specific edge generators to eliminate graph distribution shifts across federated clients, improving GNN performance.

Xingbo Fu, Zihan Chen, Yinhan He, Song Wang and 3 more

Published Apr 11, 2025 · 3 citations

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AAAI 2025Xi'an Jiaotong U Virginia Graph neural networks

Out-of-Distribution Generalization on Graphs via Progressive Inference

GPro improves graph out-of-distribution generalization by decomposing causal invariant learning into progressive inference steps and augmenting training with counterfactual samples, outperforming state-of-the-art methods by 4.91%.

Yiming Xu, Bin Shi, Zhen Peng, Huixiang Liu and 2 more

Published Apr 11, 2025 · 3 citations

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AAAI 2025U Virginia Arizona State U North Carolina at Chapel Hill Florida State Graph neural networks

BrainMAP: Learning Multiple Activation Pathways in Brain Networks

BrainMAP learns multiple brain network activation pathways via sequential models and Mixture-of-Experts, improving fMRI analysis and interpretability.

Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding and 7 more

Published Apr 11, 2025 · 2 citations

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71%Highly rated

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arXivU Virginia Visa Graph neural networks

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

Proposed framework improves graph OOD generalization by jointly enhancing environment augmentation and invariant subgraph extraction consistency.

Song Wang, Xiaodong Yang, Rashidul Islam, Huiyuan Chen and 3 more

Published Jan 7, 2025 · 0 citations

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arXivU Virginia Florida State Graph neural networks

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective

A spectral benchmark reveals that graph neural networks capture diverse frequency components through non-linear layers, not just neighborhood aggregation filters.

Yushun Dong, Patrick Soga, Yinhan He, Song Wang and 1 more

Published Dec 10, 2024 · 1 citation

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71%Highly rated

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U Virginia Visa Graph neural networks

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

A modifier unifies graph augmentation and invariant subgraph extraction to improve distribution and label consistency for graph OOD generalization.

Song Wang, Xiaodong Yang, Rashidul Islam, Huiyuan Chen and 3 more

Published Dec 9, 2024 · 3 citations

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ACM Transactions on Knowledge Discovery from Data 2024U Science and Technology of Chin National University of Singapore Graph neural networks

Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning

Causal attention learning via backdoor adjustment separates causal and shortcut graph features to improve out-of-distribution generalization of GNNs.

Yongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang and 3 more

Published Feb 6, 2024 · 31 citations

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arXivXi'an Jiaotong Graph neural networks

HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention

HOFA improves Twitter bot detection via homophily-oriented graph augmentation and frequency-adaptive attention, achieving state-of-the-art results on three benchmarks.

Sen Ye, Zhaoxuan Tan, Zhenyu Lei, He, Ruijie and 3 more

Published Jun 22, 2023 · 3 citations

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71%Highly rated

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arXivPLA Information Engineering Graph neural networks

Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection

OS-GNN generates minority-class feature-space samples via neighborhood aggregation for graph-based bot detection, outperforming baselines without edge synthesis.

Shuhao Shi, Kai Qiao, Jie Chi Yang, Baojie Song and 2 more

Published Feb 14, 2023 · 2 citations

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NeurIPS 2022U Washington Xi'an Jiaotong Graph neural networks

TwiBot-22: Towards Graph-Based Twitter Bot Detection

TwiBot-22 introduces the largest graph-based Twitter bot benchmark with high-quality annotations and evaluates 35 baselines across nine datasets.

Shangbin Feng, Zhaoxuan Tan, Herun Wan, Ningnan Wang and 18 more

Published Jun 9, 2022 · 46 citations · Code ★ 270

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NeurIPS 2026Tianjin Hebei University of Technology Jilin University, China Graph neural networks

Adaptive Attribute Completion with Representation Space for Incomplete Graph Domain Adaptation

Niya Yang, Di Jin, Zhizhi Yu, Liang Yang and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Fudan Fudan University, Tsinghua Graph neural networks

Fast Algorithms for the Label Propagation Operator on Signed Graphs

Yubo Sun, Zhongzhi Zhang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Texas at Arlington Graph neural networks

gHAWK: Structural Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs

Humera Sabir, Fatima Farooq, Ashraf Aboulnaga

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Xi'an Jiaotong Beijing University of Post and T Graph neural networks

Propagate to Discover: Graph-Structured Propagation for Generalized Category Discovery

yongqi tian, Junyong Liu, Jinkun Ran, Haoyuan He and 1 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SolverX Max Planck Institute for Mathema Universität des Saarlandes Graph neural networks

Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature

Yun Young Choi, Asung Kil, Sun Woo Park, Minho Lee and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Michigan State Georgia Institute of Technology Hippocratic AI Southern University of Science a Graph neural networks

Understanding Graph Self-Supervised Pre-training under Distribution Shifts: A Scaling Law Perspective

Bingheng Li, Shikun Liu, Yu Song, Jay Revolinsky and 4 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026South China University of Techno U Science and Technology of Chin Graph neural networks

S&P: Towards Scalable and Powerful Graph Learning with Hierarchical Structural Acquisition

Mingqi Yang, Zhaoyu Liu, Wenjie Feng

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sungkyunkwan Graph neural networks

A2I: Adjacency-to-Image Structural Encodings for Graph Learning

Heesoo Jung, Hogun Park

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026South China University Of Techno South China University of Techno Graph neural networks

Traffic STGNNs across Sensor, City, and Time Shifts: Routing Concentration Tracks Sensitivity to Sensor Dropout

XiLe Wang, Mingqi Yang

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightYantai Graph neural networks

RAMA: Resistance-Aware Multi-Hop Aggregation Graph Representation Learning for Robust Ethereum Account Classification

Zhaowei Liu, zengyang zhang, HaitaoYang, Yao Shan and 2 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026North Carolina State Pennsylvania State Scale AI Snapchat Graph neural networks

IncAgg: Efficient Memory-Enhanced Graph Learning via Incremental Aggregation

Xingyue Shi, Zhichao Hou, Jiahao Zhang, Suhang Wang and 3 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Nanjing Nanjing University / RIKEN Graph neural networks

OHATP: Graph Anomaly Detection with Orthogonal-Hyperspherical Augmentation and Topology Perception

yihang qiu, Yi Zhang, Di Xiong, Ge Gao and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Atmo, Inc.U California, Santa Barbara UCLouvain Graph neural networks

$\texttt{bispectrum}$: Selective $G$-Bispectra Made Practical

Johan Mathe, Adele Lantow, Simon Mataigne, Nina Miolane

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026EPFL - EPF Lausanne Oxford EPFL Graph neural networks

Hierarchical Graph Representation Learning with Pooling-Induced Substructures

Luca Sbicego, Xiaowen Dong, Dorina Thanou

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SpotlightFGV - EMAp Universidade Federal de Viçosa Avra FGV EMAp Universidade Federal Rural do Ri Graph neural networks

Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective

André Ribeiro Guimarães, Germano Barcelos, Amauri Souza, Diego Mesquita and 1 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Sun Yat-sen Xiamen Nanyang Technological SUN YAT-SEN UNIVERSITY Sun Yat-Sen Graph neural networks

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Networks on Large-scale Graphs

Huizhe Zhang, Jintang Li, Yuchang Zhu, Huazhen Zhong and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026POSTECH Pohang University of Science and Australian National Graph neural networks

Transductive Generalization for GNNs via Optimal Transport

MoonJeong Park, Seungbeom Lee, Kyungmin Kim, Jaeseung Heo and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Irvine Graph neural networks

Online Learning with Certified Unlearning over Graphs

Ruijie Du, Yanning Shen

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026CNRS/IRISA, Rennes, France CNRS, IRISA INRIA Graph neural networks

When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors

Adarsh Jamadandi, Nicolas Keriven, Aline Roumy

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Northeastern Fred Hutchinson Cancer Research U International Business and Eco Sichuan Terminus Group Graph neural networks

PATH: A Dual Perspective for High-quality Text-attributed Graph Learning

Yuhang Pei, Fanchun Meng, Changhu Wang, Tao Ren and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightNortheastern Tsinghua Northeastern University, China U International Business and Eco Sichuan Graph neural networks

CLEAR: Complementary Tripartite Play with Bayesian Calibration for Semi-Supervised Edge Classification

Zhipeng Sun, Fanchun Meng, Jiazhen Huang, Yongpeng Zhang and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KAIST Korea Institute for Advanced Stu Yonsei Korea Institute for Advanced Stu Graph neural networks

Implicit Neural Representations for Variational Problems on Graphons

Taeyoung Kim, Jineon Baek, Joonkyung Lee, Hongseok Yang

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026MIT Indian Institute of Technology B Graph neural networks

Network Intervention by Polling Strategic Agents

Chenyu Zhang, Rohit Parasnis, Saurabh Amin

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Institute of Statistical Mathema Onto Innovation The Institute of Statistical Mat Institute of Statistical Mathema Graph neural networks

Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM

Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PASQAL Pasqal Graph neural networks

Neutral-atom quantum features as complementary structural encodings for graph learning

Igor Sokolov, Mathieu Garrigues, Matthias Hecker, Lorenzo Moro and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026OralU Macau Northwest Polytechnical Universi Xidian U Texas Arlington Graph neural networks

Releasing Anchors from Cross-View Correspondence: Probabilistic Multi-View Anchor Graph Clustering

Zhoumin Lu, Yongbo Yu, Yu Duan, Feiping Nie and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, Irvine Graph neural networks

Robust and Hard-to-Remove GNN Watermarking via Topological Invariant Perception

JIPENG LI, Yanning Shen

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026University College London PhysicsX AIDOS Lab, Department of Compute Oxford InVivoAI Graph neural networks

Graph and Simplicial Complex Prediction Gaussian Process via Hodgelet Representations

Mathieu Alain, So Takao, Bastian Rieck, Xiaowen Dong and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Sydney Beijing University of Technology Independent U New South Wales U Twente Graph neural networks

Stage-Aware Dual Alignment for Covariate Shift in Graph Domain Adaptation

Hongwei Wen, Can Zhang, Haoyu He, Xintao Zhao and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026City University of Hong Kong U Sydney Graph neural networks

FedCAG: Federated Causality-Aware Graph Learning for Multi-Cloud Workload Forecasting

Yongcan Luo, Zhengjie Yang, Jiahao Zheng, Hao Wang and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Australian National U New South Wales Graph neural networks

Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs

Zhewei Chen, Hao Zhu, Jiaojiao Jiang, Ahad N. Zehmakan

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Imperial College London Graph neural networks

EnerGNN: Learning Optimization-Compatible Energy Functions for Exact Constrained Combinatorial Inference

Niki Triantafyllou, Maria Papathanasiou

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Zhengzhou Graph neural networks

Reasoning over Coupled Receptive Fields: Eliminating Subgraph Redundancy at Scale

Jing Yang, Bo Wen, Yuan Gao, XiaowenJiang and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Xi'an Jiaotong China Mobile System Integration Graph neural networks

GEM: A Dual-Scale Architecture for Graph-Level Hierarchical Representation Learning

Zhaowei Wu, Kaizhong Zheng, Guangmingzi Yang, Shuai Jiang and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Antwerp Graph neural networks

Caterpillar GNN: Replacing Message Passing with Graph-Level Aggregation

Marek Černý

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U British Columbia AppGate Department of Computer Science, Graph neural networks

Learning Transferable Representations from Operating System Entities via Provenance Graph Distillation

Tristan Bilot, Xueyuan Han, Thomas Pasquier

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Florida National University of Singapore Graph neural networks

SAG-Sep: Sparse Augmented Graphs and Onion-Guided Search for Rounded Capacity Cut Separation

Haoran Liu, Guanyi Wang, Yu Yang

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Xi'an University of Telecom Xidian Graph neural networks

Adaptive Feature Propagation for Attribute-Missing Graph Clustering

Xiang Long, QIANQIAN WANG

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026South China Normal The Hong Kong University of Scie City University of Hong Kong The Hong Kong University of Scie The Hong Kong University of Scie Graph neural networks

Revisiting Autoregressive GCNs for Vehicle Routing Problems

Zhipeng Zhong, Junquan Huang, Yu Huang, Boyuan Zheng and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of China Boston College Graph neural networks

How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?

Guanyu Cui, Yuhe Guo, Zhewei Wei, Hsin-Hao Su

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Oxford CISPA Helmholtz Center For Infor USI CISPA Helmholtz Center for Infor Graph neural networks

Fiedler-Regularized Causal Discovery for Sparse Connected DAGs

Amine M&#x27;Charrak, Abbavaram Gowtham Reddy, Thomas Lukasiewicz, Michael Bronstein and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look

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NeurIPS 2026China University of Mining and T China University of Mining Techn Graph neural networks

Heterogeneous Graph Federated Learning with Structure-Aware Data-Free Distillation

Lili Guo, Qi Li, Ruoyu Wang, Chao Li and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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67%Highly rated

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NeurIPS 2026Columbia U Chicago Washington University, Saint Lou Christian-Albrechts-Universität Graph neural networks

Bias, Measurement Error, and Double-Dipping: When Can GNN Convolutions Help Brain Connectome Prediction?

Tommaso Castellani, Jiaqi Li, Muriah D Wheelock, Rezwana R Razzaque and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U the Chinese Academy of Science U Illinois at Urbana-Champaign National University of Defense T Renmin University of China Beijing University of Aeronautic Graph neural networks

Dynamic Spectral Federated Graph-Level Clustering

Wenxin Zhang, Xi Xuan, Guangzhen Yao, Feng Zhou and 6 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Ben Gurion University of the Neg U Cambridge Graph neural networks

Module-Aware Optimization for Graph Neural Networks

Guy Hadad, Haggai Roitman, Moshe Eliasof, Bracha Shapira

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Higher School of Economics Yandex, HSE Yandex Yandex Research Graph neural networks

Benchmarking Graph Self-Supervised Learning for Node-Level Tasks: Insights and Strong Baseline

Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov, Artem Babenko and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026South China University of Techno Dalian University of Technology Graph neural networks

Matrix-Free Stochastic Training of Low-Rank Spectral Graph Learning via Randomized Adaptive Spectral Estimation

Mingqi Yang, Yanming Shen

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Zhengzhou SUN YAT-SEN UNIVERSITY Graph neural networks

MIRAGE: Hierarchical MI-Surrogate Regulation for Graph Contrastive Learning

Qi Teng, Xueer Wang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of China Graph neural networks

Beyond Node Sequences: Relational Diffusion for Unified Graph Learning

Xianan Wang, Wenji Hu, Chunyu Wei, Yueguo Chen

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightShanxi Southeast Taiyuan University of science an Graph neural networks

Boosting Graph Contrastive Learning via Manifold-Guided Representation Disentanglement

Zhiqiang Li, Jianqing Liang, Jie Wang, Junbiao Cui and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026OralShanghai Jiaotong Shanghai Jiao Tong Graph neural networks

UECO: A Unified Encoder with Structure-Aware Attention Mixture via Iterative Edge Evolving for Neural Combinatorial Optimization

Wenzheng Pan, Shuyi Yan, Nuoyan Chen, Yiyang Qu and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Texas at Dallas Virginia Polytechnic Institute a UT Dallas Graph neural networks

Graph Label Alignment: A Diagnostic Atlas for Graph Classification

Neelam Akula, Varun Shiralkar, Murat Kantarcioglu, Baris Coskunuzer

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026U Georgia The University of Texas at Dalla U Texas at Dallas U Central Florida UT Dallas Graph neural networks

From Nodes to Pixels: Topological and Structural Two-View Graph Imaging

Md Joshem Uddin, Soham Changani, Sai K Navuluru, Cuneyt Akcora and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U Luxemburg U Luxembourg Graph neural networks

Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs

Firas Bayram, Maxime Cordy

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Maryland U Maryland, College Park Google Research Graph neural networks

Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linearity

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz, Silvio Lattanzi and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Google research UCI U Maryland U Maryland, College Park Donald Bren School of Informatio Graph neural networks

Finite-Sample and Communication-Efficient Networked Information Aggregation

Mohammadhossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Carnegie Mellon Texas A&M University - Corpu Graph neural networks

Traversal-Invariant Positional Encoding for Serialized Graphs

Krish Mody, Sridhar Radhakrishnan, Chandra N Sekharan

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Institute of Science Tokyo Nanyang Technological Tokyo Institute of Technology Graph neural networks

Joint Certification for Attributed Graphs: Beyond Topology-Only Robustness

Blaise Delattre, Hengyu WU, Wei Yang Bryan Lim, Yang Cao

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Sharif University of Technology Graph neural networks

Exact Combinatorial Optimization for Partial Permutation Synchronization

Mohammad Mahdi Omati, Arash Amini

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Edinburgh Shenzhen Institutes of Advanced Graph neural networks

Constrained Graph Clustering: A Spectral Algorithm with Generalized Eigenvectors

Shihong Song, He Sun

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Helsinki Graph neural networks

Avoiding Feature Collapse in Graph ODEs via Hysteretic Topology Evolution

Qinhan Hou, Jing Tang

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Tongji Shanghai Jiaotong Jilin UNIVERSITY OF CALIFORNIA, SANTA Graph neural networks

Test-Time Graph Recalibration: Enhancing Robust Zero-Shot Inference for Graph Foundation Models

Chunchun Chen, Zhen Luo, Xing Wei, Yuxing Zhang and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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74%Highly rated

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NeurIPS 2026Beijing University of Posts and Beijing University of Post and T Graph neural networks

LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing

LINC explicitly computes local routing consequences to score actions via shared linear comparison and context modulation, improving neural routing baselines especially at larger scales.

ShaoFeng Qin, Li Wang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Beihang Beijing University of Aeronautic National University of Singapore Graph neural networks

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

EMC replaces generative memory synthesis with closed-form crystallization for efficient continual graph adaptation under non-stationary shifts, reducing runtime by 87.4% and GPU memory by 92.4%.

Yue Hou, Ruomei Liu, Yingke Su, Wu Junran and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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72%Highly rated

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NeurIPS 2026The Chinese University of Hong K National University of Defense T City University of Hong Kong Hefei University of Technology The Education University of Hong Graph neural networks

DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation

DisRFM embeds graph representations on constant-curvature manifolds with polar flow matching to prevent structural degeneration and optimization instability, outperforming state-of-the-art graph domain adaptation methods.

Yingxu Wang, Xinwang Liu, Siyang Gao, Mengzhu Wang and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Nanjing Hithink RoyalFlush Information N nanjing university Ant Group Alibaba Group Graph neural networks

Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering

PyAGC provides a scalable benchmark and modular library for attributed graph clustering across diverse industrial-scale datasets and metrics.

Yunhui Liu, Pengyu Qiu, Yu Xing, Peng Du and 5 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 32

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NeurIPS 2026U Fribourg, MILA U Fribourg Technische Universität München Université de Montréal; Mila U Bergen Graph neural networks

No Triangulation Without Representation: Generalization in Topological Deep Learning

Extending a manifold triangulation benchmark reveals GNNs and HOMP can saturate it with proper representations, yet existing models fail to generalize beyond combinatorial structure.

Johannes S. Schmidt, Martin Carrasco, Ernst Röell, Guy Wolf and 2 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SpotlightU Electronic Science and Technol Graph neural networks

Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting

Pre-trained GNNs exhibit spectral bias favoring large singular values, and SRP reverses this via spectral masking and null-space augmentation for state-of-the-art graph prompting.

Hanxu Yang, Yuhuan Zhao, Xiaodong He, Zhao Kang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Illinois Chicago U Illinois at Chicago U Warwick Graph neural networks

GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs

GRAPHLCP integrates graph topology and inter-node dependencies into localized conformal prediction via densification and PageRank-based structural proximity, improving conditional coverage efficiency on graphs.

Peyman Baghershahi, Fangxin Wang, Debmalya Mandal, Sourav Medya

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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80%Must read

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NeurIPS 2026CNRS UiT the Arctic University of Nor U Tromsø Graph neural networks

A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks

<|message_model|><|content_text|>The Tikhonov layer is an interpretable graph neural network layer whose learnable parameters directly reveal which node features and topological aspects drive predictions. Its closed-form propagation solves a generalized graph Tikhonov problem, yielding built-in expl

Nicolas Tremblay, Filippo Maria Bianchi, Benjamin Ricaud

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Amsterdam Universität Leipzig Computer Science Departmen, Tech U Amsterdam; Institute for Logic Graph neural networks

Expressive Power of Deep Homomorphism Networks over Relational Databases

Deep Homomorphism Networks are linked to first-order logic fragments via SQL, and experiments confirm their differing predictive power.

Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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72%Highly rated

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NeurIPS 2026U Waterloo IMDEA Software Institute Graph neural networks

Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank

Standard FISTA is asymptotically worse than ISTA for ℓ1-regularized PageRank, though over-regularized objectives with confinement yield accelerated bounds plus boundary overhead.

Kimon Fountoulakis, David Martínez-Rubio

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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76%Highly rated

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NeurIPS 2026U California, Santa Barbara Graph neural networks

Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks

Spectral sparsification bounds polynomial GNN filter and embedding perturbations by O(epsilon), preserving Gram matrices, distances, and training dynamics.

Sanjukta Krishnagopal

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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74%Highly rated

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NeurIPS 2026Oxford Brookes U Pisa Oxford Dynamics Oxford Brookes Graph neural networks

Random-Set Graph Neural Networks

Random-Set Graph Neural Networks model node-level epistemic uncertainty via belief functions to yield precise predictions and uncertainty estimates, outperforming baselines on nine graph datasets.

Tommy Woodley, Matteo Tolloso, Davide Bacciu, Shireen Kudukkil Manchingal and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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78%Highly rated

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NeurIPS 2026Indian Institute of Science, Ind Indian Institute of Technology D Graph neural networks

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems

GraMO couples graph interactions with temporal state updates in a single linear recurrence for latent interacting particle simulation, achieving lowest long-horizon prediction errors across benchmarks.

Karn Tiwari, Niladri Dutta, Prathosh AP, N M Anoop Krishnan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Cambridge Ruhr University Bochum, ELLIS Un Microsoft Ruhr University Bochum Graph neural networks

Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing

Introducing verifiable axioms for long-range graph benchmarks, this work proposes TRIP/GRIP to construct provably long-range tasks with closed-form per-range error bounds and audits existing benchmarks.

Ferran Hernandez Caralt, Simon Heilig, Adrián Bazaga, Asja Fischer and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Singapore Management U Electronic Science and Technol Southeast University, China Graph neural networks

Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

A unified framework reveals GNN under-confidence stems from final-layer weight decay and node distance, fixed by reducing decay and node-level calibration.

Jincheng Huang, Jie Xu, Ping Hu, Xiaoshuang Shi and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026INRIA - ENS Graph neural networks

Chaining 2-FWL GNNs for Combinatorial Graph Alignment

Chaining 2-FWL GNNs injects discrete combinatorial feedback via iterative ranking to solve graph alignment, outperforming classical and prior GNN baselines across synthetic and real-world benchmarks.

Marc Lelarge

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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lenient 3/5

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NeurIPS 2026UCLA Block mcgill Graph neural networks

A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion

Doubly-spectral stochastic GNN expansions yield unified uncertainty representations improving calibration, OOD detection, and distribution-shift robustness.

Fred Xu, Thomas Markovich, florence regol, Yizhou Sun

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Helmholtz Munich, TU Munich U Fribourg Université de Montréal; Mila AIDOS Lab, Department of Compute Graph neural networks

Diversity Curves for Graph Representation Learning

Diversity curves track structural spread across graph coarsening levels to yield interpretable, size-invariant graph embeddings for clustering, visualization, and comparison.

Katharina Limbeck, Nadja Häusermann, Martin Carrasco, Guy Wolf and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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medium 3/10

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NeurIPS 2026U Fribourg Johns Hopkins Technical University of Munich U Ferrara Centrale Supélec Graph neural networks

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

Rademacher complexity bounds unify GNN expressivity and input geometry through equivalence classes, covering numbers, and Wasserstein robustness.

Martin Carrasco, Caio Deberaldini Netto, Ehimare Okoyomon, Aneeqa Mehrab and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026SpotlightTechnion - Israel Institute of T Technion - Israel Institute of T IT University of Copenhagen Graph neural networks

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them

For every k, k-WL fails to distinguish some non-isomorphic simple-spectrum graphs, so PRiSM provides the first complete canonicalization of their eigendecompositions to enable universal approximation.

Snir Hordan, Nadav Dym, Tim Seppelt

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 2/5

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NeurIPS 2026Technion - Israel Institute of T Technical University of Munich Graph neural networks

Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables

Standard spectral GNNs exhibit poor signal propagation; Schrödinger GNNs route signals across graphs more effectively via observable-based modeling inspired by quantum mechanics.

Eden Nagar, Ya-Wei Eileen Lin, Ron Levie

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U Roma "Tor Vergata"U Basel U Rome Tor Vergata Bocconi UiT the Arctic University of Nor Graph neural networks

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry–Task Alignment

Hyperbolic graph neural networks outperform Euclidean models only when tasks align with hyperbolic geometry, not merely when graphs are tree-like.

Dionisia Naddeo, Jonas Linkerhägner, Nicola Toschi, Geri Skenderi and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 4/5

medium 7/10

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NeurIPS 2026OralKTH Royal Institute of Technolog Johns Hopkins Whiting School of Engineering Graph neural networks

Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning

Graph Cascades uses contagion diffusion to build auxiliary edges in linear time, boosting GNN and graph transformer accuracy on heterophilic and high-degree graphs while failing on regular low-degree graphs.

Meher Chaitanya Pindiprolu, My Le, Luana Ruiz

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Southern University of Science a Shenzhen Pengcheng Lab Graph neural networks

SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver

SPACE defines pivot-aligned coordinate-free embeddings and adaptive decoding to unify neural routing across symmetric and asymmetric VRPs, achieving strong zero-shot generalization on 110 variants.

Rongsheng Chen, Changliang Zhou, Canhong Yu, Yuanyao Chen and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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NeurIPS 2026Columbia Graph neural networks

Information Loss and Disparate Effects in Network Embeddings

Baseline network embeddings cause information loss scaling with graph density and assortativity, yielding identical limits for different SBM graphs that disproportionately raise link prediction errors for smaller, sparser communities.

Gabriel Chuang, Augustin Chaintreau

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Purdue RWTH Aachen RWTH Aachen Cornell Graph neural networks

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks

A feasibility-preserving graph neural network replaces SDP solvers in exact Max-Cut branch-and-bound, cutting bounding costs up to 10.6× versus Mosek.

Hao Chen, Chendi Qian, Christopher Morris, Andrea Lodi and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Tsinghua Graph neural networks

Just Ramp-Up: Debiasing Regression-based Estimator for A/B Tests under Network Interference

Merging data from sequential ramp-up experiments substantially reduces regression estimator bias under network interference by improving training across varying treatment proportions, especially with cluster randomization.

Qianyi Chen, Bo Li

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 5/5

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