awesome-graph-research-neurips2024
github.com/azminewasi/awesome-graph-research-neurips2024 ↗All graph/GNN papers accepted at NeurIPS 2024.
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GNN TheoriesGNNs for PDE/ODE/PhysicsGraph and Large Language Models/AgentsKnowledge Graph and Knowledge Graph EmbeddingsSpatial and/or Temporal GNNsGNN ApplicationsExplainable AIReinforcement LearningGraphs and MoleculesGFlowNetsCausal Discovery and GraphsFederated Learning, Privacy, DecentralizationScene GraphsGraphs, GNNs and EfficiencyMore Possible WorksMore Collectons:
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More Possible Works
- Accelerating ERM for data-driven algorithm design using output-sensitive techniques
- Amortized Eigendecomposition for Neural Networks
- A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization
- AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
- bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction
- Breaking the curse of dimensionality in structured density estimation
Graphs, GNNs and Efficiency
- Accelerating Non-Maximum Suppression: A Graph Theory Perspective
- Active design of two-photon holographic stimulation for identifying neural population dynamics
- Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
- Almost Surely Asymptotically Constant Graph Neural Networks
- Analysis of Corrected Graph Convolutions
- An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
GNN Theories
- Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
- Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image SynthesisDiffusion
- A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
- A Foundation Model for Zero-shot Logical Query ReasoningDiffusion
- Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport LossDiffusion
- Approximately Equivariant Neural Processes
Scene Graphs
Graph and Large Language Models/Agents
Graphs and Molecules
Causal Discovery and Graphs
- A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
- Amortized Active Causal Induction with Deep Reinforcement Learning
- A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
- Causal Discovery from Event Sequences by Local Cause-Effect Attribution
- Causal discovery with endogenous context variables
- Causal Effect Identification in a Sub-Population with Latent Variables
Showing a sample of 463 resources. View the full list on GitHub →