Using Optimizate Method(including LLM， GNNs and other classical method) to solve some problem of complex graph.
- Fan et al_2024_Generalizing Graph Neural Networks on Out-of-Distribution Graphs, TPAMI 2024
- Guang et al_2024 - Graph Convolutional Networks With Adaptive Neighborhood Awareness, TPAMI 2024
- Park et al_2024 - Forward Learning of Graph Neural Networks, ICLR 2024
- MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning, WWW 2024
- Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts, NIPS 2023
- Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization, NIPS 2023
- SPA: A Graph Spectral Alignment Perspective for Domain Adaptation, NIPS 2023
- Learning Invariant Representations of Graph Neural Networks via Cluster Generalization, NIPS 2023
- Graph-Structured Gaussian Processes for Transferable Graph Learning, NIPS 2023
- WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding, NIPS 2023
- Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization, NIPS 2023
- Universal Prompt Tuning for Graph Neural Networks, NIPS 2023
- Structural Re-weighting Improves Graph Domain Adaptation, ICML 2023
- Towards Understanding Generalization of Graph Neural Networks, ICML 2023
- (also in 图数据label稀缺-不平衡-噪声)GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels, NIPS 2023
- (also in 图数据少样本学习)CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification, ICML 2023
- Yu 等 - 2024 - Recognizing Predictive Substructures With Subgraph, TPAMI 2024
- A Graph is Worth K words Euclideanizing Graph using Pure Transformer, ICLR 2024
- Autoregressive Diffusion Model for Graph Generation, ICML 2023
- Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure, ICML 2023
- Chen 等 - 2024 - POLYGCL GRAPH CONTRASTIVE LEARNING VIA LEARNABLE, ICLR 2024
- Zheng 等 - 2024 - Node-Oriented Spectral Filtering for Graph Neural Network, ICLR 2024
- Simple and Asymmetric Graph Contrastive Learning without Augmentations, NIPS 2023
- Affinity-Aware Graph Networks, NIPS 2023
- Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?, NIPS 2023
- When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability, NIPS 2023
- Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily, NIPS 2023
- Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs, ICML 2023
- (also in 过平衡问题)GREAD: Graph Neural Reaction-Diffusion Networks, ICML 2023
- Han 等 - 2024 - STRUCTURAL FAIRNESS-AWARE ACTIVE LEARNING FOR GRAPH NEURAL NETWORKS, ICLR 2024
- Zheng 和 Wen - 2024 - ONLINE GNN EVALUATION UNDER TEST-TIME GRAPH DISTRIBUTION SHIFTS, ICLR 2024
- Zhuo 等 - 2024 - PARTITIONING MESSAGE PASSING FOR GRAPH FRAUD DETECTION, ICLR 2024
- Cost-effective Data Labelling for Graph Neural Networks, WWW 2024
- Towards Expansive and Adaptive Hard Negative Mining: Graph Contrastive Learning via Subspace Preserving, WWW 2024
- GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels, NIPS 2023
- Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First, NIPS 2023
- Towards Label Position Bias in Graph Neural Networks, NIPS 2023
- Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data, NIPS 2023
- No Change, No Gain: Empowering Graph Neural Networks with Expected Model Chang, NIPS 2023
- Wang 等 - Deep Insights into Noisy Pseudo Labeling on Graph, NIPS 2023
- Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks, NIPS 2023
- Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks, ICML 2023
- When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations, ICML 2023
- Disentangled Multiplex Graph Representation Learning, ICML 2023
- Towards Understanding and Reducing Graph Structural Noise for GNNs, ICML 2023
- CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification, ICML 2023
- Barbero 等 - 2024 - LOCALITY-AWARE GRAPH REWIRING IN GNNS, ICLR 2024
- Graph Convolutional Kernel Machine versus Graph Convolutional Networks, NIPS 2023
- Demystifying Oversmoothing in Attention-Based Graph Neural Networks, NIPS 2023
- Improving Graph Neural Networks with Learnable Propagation Operators, ICML 2023
- Towards Deep Attention in Graph Neural Networks: Problems and Remedies, ICML 2023
- Path Neural Networks: Expressive and Accurate Graph Neural Networks, ICML 2023
- Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering, ICML 2023
- GREAD: Graph Neural Reaction-Diffusion Networks, ICML 2023
- (Heterogeneous)Liu 等 - 2024 - VBH-GNN: VARIATIONAL BAYESIAN HETEROGE- NEOUS GRAPH NEURAL NETWORKS FOR CROSS- SUBJECT EMOTION RECOGNITION, ICLR 2024
- (Heterogeneous)Melton_Krishnan_2023_muxGNN: Multiplex Graph Neural Network for Heterogeneous Graphs, TPAMI 2023
- (Heterogeneous)Zhou et al_2023_SlotGAT: Slot-based Message Passing for Heterogeneous Graphs, ICML 2023
- (Hypergraph)Wang et al_2023_From Hypergraph Energy Functions to Hypergraph Neural Networks, ICML 2023
- (Hypergraph)Wang 和 Kleinberg - 2024 - FROM GRAPHS TO HYPERGRAPHS: HYPERGRAPH PROJECTION AND ITS RECONSTRUCTION, ICLR 2024
- (Hypergraph)Yan 等 - 2024 - HYPERGRAPH DYNAMIC SYSTEM, ICLR 2024
- (Hypergraph)Duta 等 - Sheaf Hypergraph Networks, NIPS 2023
- (Hypergraph)HYTREL: Hypergraph-enhanced Tabular Data Representation Learning, NIPS 2023
- (Hypergraph)CAt-Walk: Inductive Hypergraph Learning via Set Walks, NIPS 2023
- (Dynamic Graph)Towards Better Dynamic Graph Learning: New Architecture and Unified Library, NIPS 2023
- (Dynamic Graph)Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs, NIPS 2023
- (Dynamic Graph)(also in OOD问题)Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts, NIPS 2023
- (Dynamic Graph)(also in OOD问题)Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization, NIPS 2023
- (Heterogeneous)(Dynamic Graph)GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets, ICML 2023
- Fatemi 等 - 2024 - TALK LIKE A GRAPH ENCODING GRAPHS FOR LARGE LANGUAGE MODEL, ICLR 2024
- He 等 - 2024 - HARNESSING EXPLANATIONS: LLM-TO-LM INTER- PRETER FOR ENHANCED TEXT-ATTRIBUTED GRAPH REPRESENTATION LEARNING, ICLR 2024
- Liu 等 - 2024 - ONE FOR ALL: TOWARDS TRAINING ONE GRAPH MODEL FOR ALL CLASSIFICATION TASKS, ICLR 2024
- Luo 等 - 2024 - REASONING ON GRAPHS: FAITHFUL AND INTER- PRETABLE LARGE LANGUAGE MODEL REASONING, ICLR 2024
- LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings, ICLR 2024
- Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach, NIPS 2023
- Efficient Learning of Linear Graph Neural Networks via Node Subsampling, NIPS 2023
- Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling, NIPS 2023
- SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations, NIPS 2023
- Graph Neural Tangent Kernel: Convergence on Large Graphs, ICML 2023
- GOAT: A Global Transformer on Large-scale Graphs, ICML 2023
- LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation, ICML 2023
- Shin et_al_2024 - PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks, TPAMI 2024
- Provable Training for Graph Contrastive Learning, NIPS 2023
- D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion, NIPS 2023