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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Gnn-Based Detectors

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Graph Theory

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Exponential Random Graph Models

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1,049 papers

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HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks
Jun 2, 2026Zongrui Li, Yuhang Zhao, Ying Zhao +3Graph Neural Networks Explainability
Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Jun 2, 2026Anubha Goel, Juho KanniainenGraph Neural Networks Graph Representation Learning
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jun 2, 2026Jaeyoon Sim, Minjae Lee, Guorong Wu +1Alzheimer Human Brain Networks
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
Jun 2, 2026Ankang Yang, Jitao Zhao, Dongxiao He +3Graph Foundation Model Propagation
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
Jun 2, 2026Yifan Jin, Qirui Ji, Bin Qin +4Graph Foundation Model Graph-Based Retrieval Augmented Generation
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Jun 2, 2026Li Sun, Zhenhao Huang, Yiding Wang +3Graph Foundation Model Wireless Foundation Models
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Jun 2, 2026Parth Verma, Parv P. Singh, Vipul Garg +3Graph Neural Networks Continual Model Merging
GTBench: A Curriculum-Grounded Benchmark for Evaluating LLMs as Mathematical Research Assistants in Graph Theory
Jun 2, 2026Noujoud Nader, Ibrahem Aljabea, Patrick Diehl +1Reasoning Graphs Benchmarking Large Language Models
Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs
Jun 2, 2026Jaejun Lee, Joyce Jiyoung WhangMessage Passing Neural Networks Complex Networks
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
Jun 2, 2026Phillip JiangRelational Deep Learning Heterogeneous Graph Transformers
Query-Limited Community Recovery in Stochastic Block Models
Jun 1, 2026Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen +1Optimal Sample Complexity Subgraphs
Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints
Jun 1, 2026Reda Snaiki, Abdelatif MerabtineOptimal Sensor Placement Temporal Graph Neural Networks
Graph Edit Distance Formulation for the Vehicle Routing Problem: Theory and Analysis
Jun 1, 2026Adel DabahVehicle Routing Problem Maximum Independent Set
Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks
Jun 1, 2026Bryan Cheng, Austin JinStructure-Aware Graph Neural Networks
Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction
Jun 1, 2026Enqiang Zhu, Yizi Liu, Yilong Luo +3Protein Graph Neural Networks
Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance
Jun 1, 2026Maryam Gholami Shiri, Ivana Krminac, Marko Djukanović +3Shortest Paths Text-Attributed Graph
Construction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks
Jun 1, 2026Ping Li, Bartlomiej BrzozkaKnowledge Graphs History
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
Jun 1, 2026Zichao Yue, Zhiru ZhangGraph Neural Networks Propagation
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Jun 1, 2026Canyixing Cui, Tao Wu, Xingping Xian +3Graph Neural Networks Edge
TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions
Jun 1, 2026Farzaneh Heidari, Guillaume RabusseauShapley Value Tensor Networks
Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
May 31, 2026Denica Kjorvezir, Marko Djukanović, Ana Gjorgjevikj +2Benchmarking Large Language Models Semantic Similarity Metrics
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
May 31, 2026Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2Graph Neural Networks Kernel Method
Statistical Testing on Directed Graphs by Surrogate Data Generation
May 30, 2026Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De VilleExponential Random Graph Models Signal Processing
Graph Neural Networks for Predicting Solvability of Finite Groups
May 30, 2026Tal WeissblatFinite Groups Graph Neural Networks
Easy, robust approximate message passing for planted spike models
May 30, 2026Misha Ivkov, Tselil SchrammPrincipal Component Analysis Message Passing Neural Networks
On Efficient Scaling of GNNs via IO-Aware Layers Implementations
May 29, 2026Daria Fomina, Daniil Krasylnikov, Alexey Boykov +3Graph Neural Networks Graphics Processing Unit Kernels
Graphical einops: bridging tensor networks and computation graphs
May 29, 2026Vincent Wang-Maścianica, Nikhil KhatriTensor Networks Tensor Programs
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
May 29, 2026Ashley Hoi-Ting Au, Zikun Zhang, Ligang He +1Graph Representation Learning Retraining
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
May 29, 2026Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1Graph Representation Learning Graph Neural Networks
Graph Neural Networks Are Not Continuous Across Graph Resolutions
May 29, 2026Christian Koke, Yuesong Shen, Abhishek Saroha +4Graph Neural Networks Neural Network
GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
May 29, 2026Saku Peltonen, August Bøgh Rønberg, Andreas Plesner +1Graph Representations Heterogeneous Graph Transformers
An Efficient and Scalable Graph Condensation with Structure-Preserving
May 29, 2026Yulin Hu, Fuyan Ou, Ye YuanGraph Neural Networks
A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere
May 29, 2026Gopal AnantharamanGraph Neural Networks Message Passing Neural Networks
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
May 29, 2026Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9Graph Neural Networks Abstention
Reducing the GPU Memory Bottleneck with Lossless Compression for ML -- Extended
May 29, 2026Aditya K Kamath, Arvind Krishnamurthy, Marco Canini +1Large Language Model Compression Context Compression
ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
May 29, 2026Tao Feng, Chongrui Ye, Tianyang Luo +8Large Language Model Agents Hierarchical Graph Memory
Spatio-temporal stochastic graph-based learning for infectious disease forecasting
May 28, 2026Luz Stefani Sotomayor Valenzuela, Susanna Cramb, Darren WraithEpidemic Forecasting Spatio-Temporal Forecasting
Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
May 28, 2026Amirhossein Ghaffari, Saeid Sheikhi, Ekaterina GilmanTemporal Graph Neural Networks Accurate Traffic Forecasting
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
May 28, 2026Ojas Nimase, Jiate Li, Yue Zhao +1Explainability Graph Representation Learning
What drives performance in molecular MPNNs? An operator-level factorial benchmark
May 28, 2026Panyu Jiao, Shuizhou Chen, Yiheng Shen +3Message Passing Neural Networks Molecular Property Prediction
SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection
May 28, 2026Hanning Lu, Yingguang Yang, Jinwei Su +8Hypergraphs Graph Neural Networks
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
May 28, 2026Zesheng Liu, Maryam RahnemoonfarLong-Horizon Forecasting Emulators
When Do Graph Foundation Models Transfer? A Data-Centric Theory
May 28, 2026Jiajun Zhu, Ying Chen, Peihao Wang +4Graph Foundation Model Cross-Domain Shifts
The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity
May 28, 2026Mikael Møller Høgsgaard, Kasper Green Larsen, Liang-Yu ZouInterpolation Optimal Sample Complexity
Gated Graph Attention Networks with Learnable Temperature
May 28, 2026Zhongtian Ma, Hao Wu, Yexin Zhang +2Heterogeneous Graph Transformers Large-Scale Benchmark
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
May 28, 2026Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw +4Variational Inference Factor Graphs
OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources
May 28, 2026Jinheon Baek, Soyeong Jeong, Sangwoo Park +5Retrieval Strategies Retrieval Task
A Fresh Look at Lamarckian Evolution and the Baldwin Effect
May 27, 2026Inès Benito, Johannes F. Lutzeyer, Benjamin DoerrEvolutionary Search Evolution
GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
May 27, 2026Gaspard Michel, Elena V. Epure, Romain Hennequin +2Text-Attributed Graph Literature
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
May 27, 2026Nicolas Tremblay, Benjamin Ricaud, Filippo Maria BianchiGraph Neural Networks Neural Network
T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
May 27, 2026Maolin Wang, Ziting Mai, Xuhui Chen +9Graph Neural Networks Complex Networks
Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
May 27, 2026Lei Zhang, Fubo Sun, Haipeng Yang +2Graph Representation Learning Image Clustering
From Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation
May 27, 2026Juergen DietrichDrug-Drug Interactions Graph Neural Networks
GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease
May 27, 2026Leo Y. Li-Han, Ellen L. Larson, Elizabeth B. Habermann +2Patient Trajectories Graph Representation Learning
Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs
May 26, 2026Nate MacFaddenTriangulations Polytopes
EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering
May 26, 2026Zhifei Dou, Shabnam Hassani, Ou WeiDiagrams Ava-Vlm
GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing
May 26, 2026Pujun Zheng, Wanying Ren, Jiacheng Yao +2Peer Review Information Retrieval Metrics
Learning Dynamic Graph Representations through Timespan View Contrasts
May 26, 2026Yiming Xu, Zhen Peng, Bin Shi +2Temporal Graphs Generalist Graph Anomaly Detection
TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
May 26, 2026Yiming Xu, Bin Shi, Bo Dong +3Text-Attributed Graph Anti-Money Laundering
On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
May 26, 2026Malte Luttermann, Ralf Möller, Marcel GehrkeFactor Graphs Factor

Jun 2, 2026 · cs.LGJ/K move · Enter open · S save

HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

Zongrui Li, Yuhang Zhao, Ying Zhao, Yuanzhao Guo+2

School of Artificial Intelligence, Jilin University · Mohamed bin Zayed University of Artificial Intelligence

Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge. Existing explanation methods suffer from two major limitations: on the one hand, the generated explanations fail to reflect the inherent semantic hierarchy of HGNNs, resulting in a lack of fidelity to the model's internal decision-making mechanism; on the other hand, feature explanations often rely on complex search or perturbation mechanisms, leading to excessive computational complexity and poor efficiency. To address these issues, we propose HiSE, a lightweight feature-oriented interpretable model for HGNNs. HiSE achieves semantically aware feature explanations through hierarchical semantic modeling: at the semantic level, local surrogate models based on the Least Absolute Shrinkage and Selection Operator (LASSO) are employed to learn sparse feature representations under each semantic view; at the cross-semantic level, the contributions of different semantic views are adaptively characterized via KL divergence to produce a unified explanation. Extensive experiments demonstrate that HiSE outperforms existing methods in terms of fidelity, robustness, and cross-semantic explanation capability, while its lightweight framework incurs low computational overhead, enabling efficient application to large-scale, complex real-world heterogeneous graphs.

Graph Neural Networks Explainability

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