Test graph isomorphism with 1-WL for different graph classes and labelings
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Updated
Apr 1, 2024 - Python
Test graph isomorphism with 1-WL for different graph classes and labelings
Official repository for "Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings" based on the official GNN-As-Kernel repository.
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.
Code and dataset to test empirically the expressive power of graph pooling operators.
Data Challenge - Kernel methods
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).
A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
Library for the analysis of time-evolving graphs
Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".
MyLectureNotes on Pascal Welke's lecture "Graph Representation Learning" (winter term 2021/2022)
Project 1 - unifesp master's degree course
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
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"
A short review on Graph Neural Networks done during the Master's degree Mathematics, Vision, Learning (MVA) from ENS Paris-Saclay.
Ausarbeitung für das Seminar Algorithm Engineering an der TU Dortmund zum Paper "On the Power of Color Refinement" von Arvind et al.
Implementation of the algorithm described in the paper "On the Power of Color Refinement".
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