A collection of important graph embedding, classification and representation learning papers with implementations.
#计算机科学#A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
#计算机科学#Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".
#计算机科学#A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
#计算机科学#A Persistent Weisfeiler–Lehman Procedure for Graph Classification
Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Python code for "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017"
Library for the analysis of time-evolving graphs
Test graph isomorphism with 1-WL for different graph classes and labelings
#计算机科学#Official repository for "Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings" based on the official GNN-As-Kernel repository.
#算法刷题#Implementation of the algorithm described in the paper "On the Power of Color Refinement".
#算法刷题#Ausarbeitung für das Seminar Algorithm Engineering an der TU Dortmund zum Paper "On the Power of Color Refinement" von Arvind et al.
Project 1 - unifesp master's degree course
A short review on Graph Neural Networks done during the Master's degree Mathematics, Vision, Learning (MVA) from ENS Paris-Saclay.
An implementation of the Weisfeiler-Leman graph isomorphism test
#计算机科学#The goal here is to use a graph kernel and a manifold learning technique in conjunction with Support Vector Machines to enhance the SVM classification.
MyLectureNotes on Pascal Welke's lecture "Graph Representation Learning" (winter term 2021/2022)
Data Challenge - Kernel methods