1. GNN

     探究模型表达能力

  • How Powerful are K-hop Message Passing Graph Neural Networks

  • Ordered Subgraph Aggregation Networks

  • Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited

  • Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks

  • Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective

  • Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

  • A Practical, Progressively-Expressive GNN

    泛化性分析

  • Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

    减少Message Passing中的冗余计算

  • Redundancy-Free Message Passing for Graph Neural Networks

    可扩展性

  • Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

    捕获长距离依赖

  • Capturing Graphs with Hypo-Elliptic Diffusions

  • MGNNI: Multiscale Graph Neural Networks with Implicit Layers

    强化节点表征（通过引入结构，距离特征，etc）

  • Geodesic Graph Neural Network for Efficient Graph Representation Learning

  • Template based Graph Neural Network with Optimal Transport Distances

  • Pseudo-Riemannian Graph Convolutional Networks

  • Neural Approximation of Extended Persistent Homology on Graphs

  • GraphQNTK: the Quantum Neural Tangent Kernel for Graph Data

    模型结构设计

  • Graph Scattering beyond Wavelet Shackles

  • Equivariant Graph Hierarchy-based Neural Networks

    优化梯度流向

  • Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again

    Library

  • Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks

    2. Graph Transformer

  • Recipe for a General, Powerful, Scalable Graph Transformer

  • Hierarchical Graph Transformer with Adaptive Node Sampling

  • Pure Transformers are Powerful Graph Learners

  • Periodic Graph Transformers for Crystal Material Property Prediction

    3. 过平滑

  • Not too little, not too much: a theoretical analysis of graph (over)smoothing

  • Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

    4. 图对比学习，图自监督

  • Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

  • Uncovering the Structural Fairness in Graph Contrastive Learning

  • Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

  • Decoupled Self-supervised Learning for Non-Homophilous Graphs

  • Understanding Self-Supervised Graph Representation Learning from a Data-Centric Perspective

  • Co-Modality Imbalanced Graph Contrastive Learning

  • Graph Self-supervised Learning with Accurate Discrepancy Learning

  • Contrastive Graph Structure Learning via Information Bottleneck for Recommendation

  • Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

  • Does GNN Pretraining Help Molecular Representation?

    5. 分布偏移以及OOD问题

  • Learning Invariant Graph Representations Under Distribution Shifts

  • Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift

  • Association Graph Learning for Multi-Task Classification with Category Shifts

  • Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

  • Towards Debiased Learning and Out-of-Distribution Detection for Graph Data

  • SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks

  • Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks

    6. 生成式模型

  • Deep Generative Model for Periodic Graphs

  • An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

  • AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators

  • Evaluating Graph Generative Models with Contrastively Learned Features

  • Molecule Generation by Principal Subgraph Mining and Assembling

  • A Variational Edge Partition Model for Supervised Graph Representation Learning

  • Symmetry-induced Disentanglement on Graphs

    7. 元学习

  • Graph Few-shot Learning with Task-specific Structures

    8. 解释性

  • Task-Agnostic Graph Explanations

  • Explaining Graph Neural Networks with Structure-Aware Cooperative Games

    9. 知识蒸馏

  • Geometric Distillation for Graph Networks

  • Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks

    10. 因果

  • Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

  • CLEAR: Generative Counterfactual Explanations on Graphs

  • Counterfactual Fairness with Partially Known Causal Graph

  • Large-Scale Differentiable Causal Discovery of Factor Graphs

  • Multi-agent Covering Option Discovery based on Kronecker Product of Factor Graphs

    11. 池化

  • High-Order Pooling for Graph Neural Networks with Tensor Decomposition

  • Graph Neural Networks with Adaptive Readouts

    12. 推荐系统

  • Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy

    13. 鲁棒性

  • Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

  • Robust Graph Structure Learning over Images via Multiple Statistical Tests

  • Are Defenses for Graph Neural Networks Robust?

  • Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats

  • EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

  • On the Robustness of Graph Neural Diffusion

  • What Makes Graph Neural Networks Miscalibrated?

  • Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

    14. 强化学习

  • DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning

  • Non-Linear Coordination Graphs

    15. 隐私保护

  • CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference

  • Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

  • Private Graph Distance Computation with Improved Error Rate

    16. 各种类型的图

    异质图

  • Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

  • Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks

    异配图

  • Revisiting Heterophily For Graph Neural Networks

  • Simplified Graph Convolution with Heterophily

    超图

  • Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model

  • Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

  • SHINE: SubHypergraph Inductive Neural nEtwork

    动态图（dynamic graphs）

  • Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs

    时空图

  • Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

  • Provably expressive temporal graph networks

  • AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs

    有向图

  • Iterative Structural Inference of Directed Graphs

  • Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture

  • Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings

  • Neural Topological Ordering for Computation Graphs

    二部图

  • Learning Bipartite Graphs: Heavy Tails and Multiple Components

    Feedback graphs

  • Learning on the Edge: Online Learning with Stochastic Feedback Graphs

  • Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs

  • Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality

    知识图谱

  • Contrastive Language-Image Pre-Training with Knowledge Graphs

  • Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

  • OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport

  • Inductive Logical Query Answering in Knowledge Graphs

  • Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graph

  • Few-shot Relational Reasoning via Pretraining of Connection Subgraph Reconstruction

  • ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

    17. 下游任务

    链接预测

  • OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs

  • A Universal Error Measure for Input Predictions Applied to Online Graph Problems

  • Parameter-free Dynamic Graph Embedding for Link Prediction

    图分类

  • Label-invariant Augmentation for Semi-Supervised Graph Classification

    图聚类

  • Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions

  • S3GC: Scalable Self-Supervised Graph Clustering

  • Stars: Tera-Scale Graph Building for Clustering and Learning

  • Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth

    图像分类

  • Vision GNN: An Image is Worth Graph of Nodes

    异常值检测

  • Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection

    分子图

  • ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

    时间序列预测

  • Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

    电路图

  • Versatile Multi-stage Graph Neural Network for Circuit Representation

  • NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis

    Robot manipulation

  • Learning-based Manipulation Planning in Dynamic Environments Using GNNs and Temporal Encoding

    18. Algorithms

    Objective-space decomposition algorithms（ODAs）

  • Graph Learning Assisted Multi-Objective Integer Programming

    Dynamic Programming （DP）

  • Graph Neural Networks are Dynamic Programmers

    Bandits

  • Graph Neural Network Bandits

  • Maximizing and Satisficing in Multi-armed Bandits with Graph Information

    Link selection

  • Learning to Navigate Wikipedia with Graph Diffusion Models

    Graph search

  • Graph Reordering for Cache-Efficient Near Neighbor Search

    Densest subgraph problem (DSG) and the densest subgraph local decomposition problem

  • Faster and Scalable Algorithms for Densest Subgraph and Decomposition

    Optimization

  • Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization

    Dimension Reduction

  • A Probabilistic Graph Coupling View of Dimension Reduction

    Physics

  • Learning Rigid Body Dynamics with Lagrangian Graph Neural Network

  • PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery

  • Physics-Embedded Neural Networks: -Equivariant Graph Neural PDE Solvers

    图相似度计算

  • Efficient Graph Similarity Computation with Alignment Regularization

  • GREED: A Neural Framework for Learning Graph Distance Functions

    NP-Hard problems

  • Learning NP-Hard Joint-Assignment planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-iteration

  • Learning to Compare Nodes in Branch and Bound with Graph Neural Networks

    19. Miscellaneous

  • Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

  • Learning on Arbitrary Graph Topologies via Predictive Coding

  • Graph Agnostic Estimators with Staggered Rollout Designs under Network Interference

  • Exact Shape Correspondence via 2D graph convolution

  • Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

  • Thinned random measures for sparse graphs with overlapping communities

  • Learning Physical Dynamics with Subequivariant Graph Neural Networks

  • On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs

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