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).

The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".

A list of data mining and machine learning papers that I implemented in 2019.

Machine Learning using marginalized graph kernel for chemical molecules.

all-paths graph kernel for protein-protein interaction extraction

A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).

A package for downloading and working with graph datasets

Assignment for the course of Artificial Intelligence: Knowledge Representation and Planning

The aim of this project is to compare the performance of an SVM trained on the different graph kernel, with or without the manifold learning step, on the following data-sets

Shortest-Path Kernel analysis with LLE and Isomap manifold algorithms application