NIPS2022上的图神经网络相关论文总结

    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