graph-com Goto Github PK

a3d3-institute.github.io

draft website using html5up template

bayesian_inference_based_gnn

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

benchmark_for_dgrl_in_hardwares

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

caw

co_proxydesign

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

dig_milp

The official implementation of DIG-MILP

distance-encoding

Distance Encoding for GNN Design

dsfm-with-incidence-relations

Decomposable Submodular Function Minimization with Incidence Relations

ed-hnn

[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li

gad-ebm

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

gad-nr

[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

gess

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

gib

Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs

gprgnn

graphmaker

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

gsat

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

gssc

[Preprint] Graph State Space Convolution (GSSC)

hept

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

inhclustering

ipm-for-submodular-hypergraphs

Inverse power method for submodular hypergraphs

labs-for-machine-learning-on-graphs

layerdag

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

lri

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

meta_co

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

multi-q-maglap

neighborhood-aware-temporal-network

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

neural_higher-order_pattern_prediction

nlb

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

pair-align

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

peg