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

A scikit-learn compatible library for graph kernels

This repository contains the "tensorflow" implementation of our paper "graph2vec: Learning distributed representations of graphs".

A convolutional neural network for graph classification in PyTorch

Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks')

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

Deriving Neural Architectures from Sequence and Graph Kernels

Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.

Contains the code (and working vm setup) for our KDD MLG 2016 paper titled: "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs"

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

Official code for Fisher information embedding for node and graph learning (ICML 2023)

Source code for our IEEE ICDM 2016 paper "Faster Kernels for Graphs with Continuous Attributes".

Isotropic Gaussian Processs on Finite Spaces of Graphs (AISTATS 2023)

An enchiridion for instructing mortals in the hidden arts of topological data analysis

Shall I work with them? A ‘knowledge graph’-based approach for predicting future research collaborations