Graph Neural Networks

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Period ending 2026-09-21

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Graph Neural Networks

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Graph Representations

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Graph Representation Learning

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Heterogeneous Graph Transformers

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Graph Foundation Model

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Maximum Independent Set

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Text-Attributed Graph

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Message Passing Neural Networks

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Subgraphs

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Graph Theory

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Gnn-Based Detectors

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Exponential Random Graph Models

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Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction
Mar 20, 2025Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can +6Alzheimer Human Brain Networks
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
Mar 19, 2025Valentin Lombard, Julien Nguyen Van, Sergei Grudinin +1Protein Data Bank Conformational Ensembles
GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN
Mar 16, 2025Pingfei Zhu, Hongyi Liu, Xueyan Liu +2Vaccination Opinion Dynamics Graph Neural Networks
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Mar 5, 2025Xi Zhu, Haochen Xue, Ziwei Zhao +7Graph Foundation Model Text-Attributed Graph
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Mar 5, 2025Shujie Li, Yuxia Wu, Yuan Fang +1Text-Attributed Graph Graph Neural Networks
GraphChase: A Platform and Benchmark for Urban Network Security Games
Jan 29, 2025Shuxin Zhuang, Shuxin Li, Tianji Yang +4Graph Representations Classical Solvers
MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes
Nov 29, 2024H. Çağrı Bilgi, Kubilay AtasuGnn-Based Detectors Message Passing Neural Networks
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sep 9, 2024Sitao Luan, Qincheng Lu, Chenqing Hua +3Graph Homophily Graph Neural Networks
Towards Complete Causal Explanation with Expert Knowledge
Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićGraph Theory Causal Reasoning
GENIE: Watermarking Graph Neural Networks for Link Prediction
Jun 7, 2024Venkata Sai Pranav Bachina, Aaryan Ajay Sharma, Ankit Gangwal +1Graph Neural Networks Zero-Shot Link Prediction
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Graph Representation Learning Distribution Shifts
Graph Unitary Message Passing
Mar 17, 2024Haiquan Qiu, Quanming YaoMessage Passing Neural Networks Graph Neural Networks
A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
Mar 13, 2024Raffaele Marino, Lorenzo Buffoni, Bogdan ZavalnijMaximum Independent Set Quantum Advantage
Provable Filter for Real-world Graph Clustering
Mar 6, 2024Xuanting Xie, Erlin Pan, Zhao Kang +2Text-Attributed Graph Graph Neural Networks
Semidefinite programming relaxations and debiasing for MAXCUT-based clustering
Jan 16, 2024Shuheng ZhouImage Clustering Semidefinite Programming
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
Nov 1, 2023Hongrui Gao, Yawen Li, Meiyu Liang +2Graph Representation Learning Graph Neural Networks
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
Jun 22, 2023Jie Gao, Yawen Li, Zhe Xue +1Partitioning Spatiotemporal
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Jun 8, 2023Yuxiang Zhang, Chengguo Yuan, Xiao Wang +2Graph Representation Learning
NervePool: A Simplicial Pooling Layer
May 10, 2023Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2Product Spaces Graph Representations
Adversarial Rademacher Complexity of Deep Neural Networks
Nov 27, 2022Jiancong Xiao, Yanbo Fan, Ruoyu Sun +1Adversarial Training Generalization Bounds
Parametrized Power-Iteration Clustering for Directed Graphs
Oct 1, 2022Gwendal Debaussart-Joniec, Harry Sevi, Matthieu Jonckheere +1Image Clustering Graph Representations
No One Knows the State of the Art in Geospatial Foundation Models
Date pendingIsaac Corley, Nils Lehmann, Caleb Robinson +6Geospatial Foundation Models Foundation Model
On Universality of Non-Separable Approximate Message Passing Algorithms
Date pendingMax Lovig, Tianhao Wang, Zhou FanApproximation Message Passing Neural Networks
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
Date pendingZhenghang Song, Tang Qian, Lu Chen +7Wireless Foundation Models Graph Foundation Model
Multi-Source Wasserstein Distributionally Robust Graph Learning
Date pendingChuansen Peng, Yifan Xia, Jinshan Zhong +1Graph Representation Learning Wasserstein Distance

Mar 20, 2025 · cs.LGJ/K move · Enter open · S save

Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction

Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can, Christelle Schneuwly Diaz+5

Dementia affects over 55 million people worldwide, projected to reach 139 million by 2050, with Alzheimer's disease (AD) accounting for 60-70% of cases. AD is associated with disruptions in metabolic brain connectivity. Detecting these disruptions early is crucial for AD management. FDG-PET is a useful tool for identifying such impairments. However, most studies rely on group-level analyses or thresholding, potentially masking individual differences and overlooking weaker yet biologically critical brain connections. Moreover, AD prediction largely focuses on univariate rather than multivariate outcomes. To address this, we introduce explainable graph-theoretical machine learning (XGML), a framework for constructing individual metabolic brain graphs and identifying subgraphs most predictive of multivariate disease-related outcomes. Using Alzheimer's Disease Neuroimaging Initiative (ADNI) FDG-PET data, we compared six graph representations against three non-graph baselines, each with six machine learning models using repeated stratified 3-fold cross-validation (10 repeats). The best configuration combined kernel density estimation with Hellinger distance and random forest. Across eight cognitive scores, it reached an overall Fisher-z-averaged Pearson correlation of r=0.595, with strongest performance for ADAS13 (r=0.67), ADAS11 (r=0.65), and ADASQ4 (r=0.62). We identified key edges that were jointly but differentially predictive across outcomes, suggesting their potential as network biomarkers of cognitive decline. Preliminary external feasibility validation on an OASIS3 cohort yielded weak predictive performance for CDRSB (r=0.26) and MMSE (r=0.18), likely reflecting cohort, protocol, and diagnostic differences. Overall, our results suggest the promise of graph-theoretical machine learning for biomarker discovery, disease prediction, and understanding the neural mechanisms underlying AD.

Alzheimer Human Brain Networks Biomarker Mild Cognitive Impairment Graph Representation Learning Machine Learning

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