Final assignment of EE226 course in SJTU by Group 12

Graph Embedding Evaluation / Code and Datasets for "Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations"

A simple program that can perform social network analysis tasks on graph data.

code implementation of GNNs in few-shot learning: GCN, GAT, GraphSAGE to the node classification task.

ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation

Graph Attention Networks (GATs) for node classification and regression tasks

Code for PRL paper: "GA-GWNN: Generalized Adaptive Graph Wavelet Neural Network"

a method to count the source node,sink node,and driver node in a graph

Source code for NeurIPS 2020 paper "Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding"

Work we did for a practical course in graph learning, organized by department Informatik 7 at RWTH University

NACFormer_MS model

KIN (K Immediate Neighbors) is a machine learning algorithm inspired by KNN (K Nearest Neighbors) which has been adjusted to graph-structured data.

Empirical Research over the possible advantages of pretraining a Graph Neural Network for Classification by using Link Prediction. We used GCN, GAT and GraphSAGE with minibatch generation. Done for the Learning From Networks course taught by professor Fabio Vandin at the University of Padova

Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)

learning GNNs

Node Classification on large Knowledge Graphs of Cora Dataset using Graph Neural Network (GNN) in Pytorch.

This repository provides code and data for the paper "GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection" (NeurIPS 2023).

Graph Equilibrium Networks: Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models

Graph Convolutional Neural Network for Atom Classification

Deep Attributed Graph Embedding