Implementation of the algorithm described in the paper "On the Power of Color Refinement".
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Updated
Mar 21, 2017 - C++
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.
A short review on Graph Neural Networks done during the Master's degree Mathematics, Vision, Learning (MVA) from ENS Paris-Saclay.
Python code for "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017"
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
Project 1 - unifesp master's degree course
MyLectureNotes on Pascal Welke's lecture "Graph Representation Learning" (winter term 2021/2022)
Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".
Library for the analysis of time-evolving graphs
A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
A collection of important graph embedding, classification and representation learning papers with implementations.
Data Challenge - Kernel methods
Code and dataset to test empirically the expressive power of graph pooling operators.
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.
Official repository for "Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings" based on the official GNN-As-Kernel repository.
Test graph isomorphism with 1-WL for different graph classes and labelings
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