weisfeiler-lehman

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 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"

1-Weisfeiler-Lehman (1-WL) color refinement graph kernel algorithm computing canonical subtree signatures to test graph isomorphism in O(E).

1-Weisfeiler-Lehman (1-WL) color refinement graph kernel algorithm computing canonical subtree signatures to test graph isomorphism in O(E).

Library for the analysis of time-evolving graphs

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

DRESS: A Continuous Framework for Structural Graph Refinement

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

1-WL Graph Isomorphism Test

This project implements a Machine Learning pipeline for chemical reaction classification using Support Vector Machines (SVM) and Weisfeiler-Leman (WL) Graph Kernels.