NIPS2022上的图神经网络相关论文总结
1. GNN
1.1 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
1.2 泛化性分析
- Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
1.3 减少Message Passing中的冗余计算
- Redundancy-Free Message Passing for Graph Neural Networks
1.4 可扩展性
- Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
1.5 捕获长距离依赖
- Capturing Graphs with Hypo-Elliptic Diffusions
- MGNNI: Multiscale Graph Neural Networks with Implicit Layers
1.6 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
1.7 模型结构设计
- Graph Scattering beyond Wavelet Shackles
- Equivariant Graph Hierarchy-based Neural Networks
1.8 优化梯度流向
- Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again
1.9 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
16.1 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
16.2 异配图
- Revisiting Heterophily For Graph Neural Networks
- Simplified Graph Convolution with Heterophily
16.3 超图
- Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model
- Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
- SHINE: SubHypergraph Inductive Neural nEtwork
16.4 动态图(dynamic graphs)
- Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
16.5 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
16.6 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
16.7 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
16.8 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
17.1 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
17.2 图分类
- Label-invariant Augmentation for Semi-Supervised Graph Classification
17.3 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
17.4 图像分类
- Vision GNN: An Image is Worth Graph of Nodes
17.5 异常值检测
- Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
17.6 分子图
- ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
17.7 时间序列预测
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
17.8 电路图
- Versatile Multi-stage Graph Neural Network for Circuit Representation
- NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
17.9 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
因上求缘,果上努力~~~~ 作者:图神经网络,转载请注明原文链接:https://www.cnblogs.com/BlairGrowing/p/16806120.html