NIPS2022上的图神经网络相关论文总结

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 Prediction3. 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 GNNs4. 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 Networks6. 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 Graphs7. 元学习
• Graph Few-shot Learning with Task-specific Structures8. 解释性
• Task-Agnostic Graph Explanations
• Explaining Graph Neural Networks with Structure-Aware Cooperative Games9. 知识蒸馏
• Geometric Distillation for Graph Networks
• Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks10. 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 Graphs11. 池化
• High-Order Pooling for Graph Neural Networks with Tensor Decomposition
• Graph Neural Networks with Adaptive Readouts12. 推荐系统
• Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy13. 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 Networks14. 强化学习
• DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
• Non-Linear Coordination Graphs15. 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 Rate16. 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 Perspective17. 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 Encoding18. 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 Networks19. 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

Original: https://blog.csdn.net/jkokj23153/article/details/126950388
Author: 刘大彪
Title: NIPS2022上的图神经网络相关论文总结