A collection of important graph embedding, classification and representation learning papers with implementations.

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".

A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

Code and dataset to test empirically the expressive power of graph pooling operators.

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"

Test graph isomorphism with 1-WL for different graph classes and labelings

Library for the analysis of time-evolving graphs

Learning to Aggregate on Structured Data

Official repository for "Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings" based on the official GNN-As-Kernel repository.

Implementation of the algorithm described in the paper "On the Power of Color Refinement".

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.

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