/NIPS2022--GNN

NIPS2022--GNN

Benchmarks

  • A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking Paper Code

  • Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs Paper Code

  • CGLB: Benchmark Tasks for Continual Graph Learning Paper Code

  • Long Range Graph Benchmark Paper Code

  • K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions Paper Code

Papers

Expressive power of GNN

  • How Powerful are K-hop Message Passing Graph Neural Networks Paper
  • Ordered Subgraph Aggregation Networks Paper
  • Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited Paper
  • Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks Paper
  • Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective Paper
  • Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries Paper
  • A Practical, Progressively-Expressive GNN Paper
  • Generalization Analysis of Message Passing Neural Networks on Large Random Graphs Paper
  • Redundancy-Free Message Passing for Graph Neural Networks Paper
  • Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity Paper
  • Task-Agnostic Graph Explanations
  • Explaining Graph Neural Networks with Structure-Aware Cooperative Games

New model architecture of GNN

  • Geodesic Graph Neural Network for Efficient Graph Representation Learning Paper
  • Template based Graph Neural Network with Optimal Transport Distances Paper
  • Pseudo-Riemannian Graph Convolutional Networks Paper
  • Neural Approximation of Extended Persistent Homology on Graphs Paper Spotlight
  • GraphQNTK: the Quantum Neural Tangent Kernel for Graph Data Paper
  • Graph Scattering beyond Wavelet Shackles Paper
  • Equivariant Graph Hierarchy-based Neural Networks Paper
  • Old can be Gold: Better Gradient Flow can make Vanilla-GCNs Great Again Paper

Graph Transformer

  • Recipe for a General, Powerful, Scalable Graph Transformer Paper
  • Hierarchical Graph Transformer with Adaptive Node Sampling Paper
  • Pure Transformers are Powerful Graph Learners Paper
  • Periodic Graph Transformers for Crystal Material Property Prediction Paper

Graph Contrastive Learning / Self-supervised Learning

  • Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination Paper
  • Uncovering the Structural Fairness in Graph Contrastive Learning Paper
  • Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum Paper
  • Decoupled Self-supervised Learning for Non-Homophilous Graphs Paper
  • Analyzing Data-Centric Properties for Graph Contrastive Learning Paper
  • Co-Modality Graph Contrastive Learning for Imbalanced Node Classification Paper
  • Graph Self-supervised Learning with Accurate Discrepancy Learning Paper
  • Contrastive Graph Structure Learning via Information Bottleneck for Recommendation Paper
  • Does GNN Pretraining Help Molecular Representation? Paper

Robustness / Privacy

  • Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias Paper
  • Robust Graph Structure Learning over Images via Multiple Statistical Tests Paper
  • Are Defenses for Graph Neural Networks Robust? Paper
  • Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats Paper
  • EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks Paper
  • On the Robustness of Graph Neural Diffusion to Topology Perturbations Paper
  • What Makes Graph Neural Networks Miscalibrated? Paper
  • Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks Paper
  • CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference Paper
  • Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Paper
  • Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate Paper

Oversmooth / Oversquashing

  • Not too little, not too much: a theoretical analysis of graph (over)smoothing
  • Capturing Graphs with Hypo-Elliptic Diffusions
  • MGNNI: Multiscale Graph Neural Networks with Implicit Layers

Heterogeneous Graph

  • Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks Paper
  • Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks Paper
  • Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering Paper

Heterophily Graph

  • Revisiting Heterophily For Graph Neural Networks Paper
  • Simplified Graph Convolution with Heterophily Paper
  • Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs Paper

Hyper Graph

  • Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model Paper
  • Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative Paper
  • SHINE: SubHypergraph Inductive Neural nEtwork Paper

Other types of Graph

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

Knowledge Graph

  • 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

Downstream Task

  • 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
  • Label-invariant Augmentation for Semi-Supervised Graph Classification
  • 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
  • Vision GNN: An Image is Worth Graph of Nodes
  • Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
  • ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
  • Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
  • Versatile Multi-stage Graph Neural Network for Circuit Representation
  • NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
  • Learning-based Manipulation Planning in Dynamic Environments Using GNNs and Temporal Encoding

GNN based Algorithms

  • Graph Learning Assisted Multi-Objective Integer Programming
  • Graph Neural Networks are Dynamic Programmers
  • Graph Neural Network Bandits
  • Maximizing and Satisficing in Multi-armed Bandits with Graph Information
  • Learning to Navigate Wikipedia with Graph Diffusion Models
  • 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
  • Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization
  • A Probabilistic Graph Coupling View of Dimension Reduction
  • 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
  • Efficient Graph Similarity Computation with Alignment Regularization
  • GREED: A Neural Framework for Learning Graph Distance Functions
  • 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

Distribution Shifts

  • 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

Generative model

  • 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

Causal Substructure

  • 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

Pooling

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

Others

  • 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
  • Graph Few-shot Learning with Task-specific Structures
  • Geometric Distillation for Graph Networks
  • Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks
  • Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy
  • DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
  • Non-Linear Coordination Graphs
  • Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks