Pinned Repositories
ED-HNN
[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li
GAD-NR
[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
GraphMaker
[TMLR] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
GSAT
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
LRI
[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.
Neighborhood-Aware-Temporal-Network
Yuhong Luo and Pan Li. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs, 2022.
PEG
StruRW
[ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
SubgraphRAG
Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
SUREL
[VLDB'22] SUREL is a novel walk-based computation framework for efficient subgraph-based graph representation learning.
Graph-COM's Repositories
Graph-COM/GSAT
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
Graph-COM/GraphMaker
[TMLR] GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
Graph-COM/ED-HNN
[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li
Graph-COM/GAD-NR
[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
Graph-COM/PEG
Graph-COM/Neighborhood-Aware-Temporal-Network
Yuhong Luo and Pan Li. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs, 2022.
Graph-COM/SubgraphRAG
Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
Graph-COM/LRI
[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.
Graph-COM/StruRW
[ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
Graph-COM/SUREL
[VLDB'22] SUREL is a novel walk-based computation framework for efficient subgraph-based graph representation learning.
Graph-COM/HEPT
[ICML'24 Oral] LSH-Based Efficient Point Transformer (HEPT)
Graph-COM/SUREL_Plus
[VLDB'23] SUREL+ is a novel set-based computation framework for scalable subgraph-based graph representation learning.
Graph-COM/CO_ProxyDesign
The repository for 'Unsupervised Learning for Combinatorial Optimization with Principled Proxy Design'
Graph-COM/GESS
Code for GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
Graph-COM/DIG_MILP
The official implementation of DIG-MILP
Graph-COM/GSSC
[Preprint] Graph State Space Convolution (GSSC)
Graph-COM/Pair-Align
[ICML 2024] Code for Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)
Graph-COM/SPE
Official code for SPE
Graph-COM/LayerDAG
LayerDAG: A Layerwise Autoregressive Diffusion Model of Directed Acyclic Graphs
Graph-COM/Meta_CO
the official repository of the paper unsupervised learning for combinatorial optimization needs meta learning
Graph-COM/Bayesian_inference_based_GNN
[Neurips2022] Understanding Non-linearity in Graph Neural Networks from the Perspective of Bayesian Inference
Graph-COM/Benchmark_for_DGRL_in_Hardwares
The official implementation of `A benchmark for Directed Graph Representation Learning in Hardware Designs'
Graph-COM/NLB
"No Need to Look Back: An Efficient and Scalable Approach for Temporal Network Representation Learning" by Yuhong Luo and Pan Li
Graph-COM/GAD-EBM
[NeurIPS 2023 : GLFRONTIERS Workshop] GAD-EBM : Graph Anomaly Detection using Energy-Based Models
Graph-COM/Multi-q-Maglap
Graph-COM/xgdl
Explainability Library with Geometric Deep Learning for Scientific Tasks
Graph-COM/Langevin_unlearning
Graph-COM/PvGaLM
PvGaLM is a novel privacy-preserving pipeline for relational learning.
Graph-COM/SGD_unlearning
Graph-COM/Labs-for-Machine-Learning-on-Graphs