Graph-COM

GSAT

[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.

GraphMaker

code for the paper "GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?"

PEG

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

Neighborhood-Aware-Temporal-Network

Yuhong Luo and Pan Li. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs, 2022.

StruRW

[ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)

LRI

[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.

SUREL

[VLDB'22] SUREL is a novel walk-based computation framework for efficient subgraph-based graph representation learning.

HEPT

[ICML'24 Oral] LSH-Based Efficient Point Transformer (HEPT)

CO_ProxyDesign

The repository for 'Unsupervised Learning for Combinatorial Optimization with Principled Proxy Design'

SUREL_Plus

[VLDB'23] SUREL+ is a novel set-based computation framework for scalable subgraph-based graph representation learning.

GESS

Code for GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts

Neural_Higher-order_Pattern_Prediction

DIG_MILP

The official implementation of DIG-MILP

Pair-Align

[ICML 2024] Code for Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)

SPE

Official code for SPE

Bayesian_inference_based_GNN

[Neurips2022] Understanding Non-linearity in Graph Neural Networks from the Perspective of Bayesian Inference

Meta_CO

the official repository of the paper unsupervised learning for combinatorial optimization needs meta learning

GSSC

[Preprint] Graph State Space Convolution (GSSC)

NLB

"No Need to Look Back: An Efficient and Scalable Approach for Temporal Network Representation Learning" by Yuhong Luo and Pan Li

Benchmark_for_DGRL_in_Hardwares

The official implementation of `A benchmark for Directed Graph Representation Learning in Hardware Designs'

GAD-EBM

[NeurIPS 2023 : GLFRONTIERS Workshop] GAD-EBM : Graph Anomaly Detection using Energy-Based Models

LayerDAG

code for the paper "LayerDAG: A Layerwise Autoregressive Diffusion Model of Directed Acyclic Graphs"

Multi-q-Maglap

xgdl

Explainability Library with Geometric Deep Learning for Scientific Tasks

a3d3-institute.github.io

draft website using html5up template

distance-encoding

Distance Encoding for GNN Design

GPRGNN

Labs-for-Machine-Learning-on-Graphs