[The Web Conference 2024] GNNShap: Scalable and Accurate GNN Explanation using Shapley Values

A game theoretic approach to explain the output of any machine learning model.

Kolmogorov Arnold Networks

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"

Papers about graph transformers.

Model Log 是一款基于 Python3 的轻量级机器学习(Machine Learning)、深度学习(Deep Learning)模型训练评估指标可视化工具，可以记录模型训练过程当中的超参数、Loss、Accuracy、Precision、F1值等，并以曲线图的形式进行展现对比，轻松三步即可实现。

Fuzzy Deep Neural Network.

Unsupervised Domain Adaptation on Graphs

PyTorch implementation of AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

[ICLR 2022] Graph-Relational Domain Adaptation

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially label…

Implementation of the Knowledge Transferable Graph Neural Network (KTGNN), published on WWW2023

[Paper][SIGIR2022] Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

Disease subnetwork detection with explainable Graph Neural Networks

StellarGraph - Machine Learning on Graphs

Ensemble learning with graph neural networks for disease module discovery and classification

Code of GAMLP for Open Graph Benchmark. KDD‘22

An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))

Source codes for our IJCAI 2021 paper "User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation"

CIKM 2021: Pooling Architecture Search for Graph Classification

The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields

Second-Order Pooling for Graph Neural Networks

AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Graph Neural Network with Hierarchical Pooling for PyTorch: "Hierarchical Graph Representation Learning with Differentiable Pooling".

Pytorch implementation of Graph U-Nets (ICML19)

Hierarchical Multi-View Graph Pooling with Structure Learning (TKDE-2021)