all-paths graph kernel for protein-protein interaction extraction

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

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

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

A package for downloading and working with graph datasets

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

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

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

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

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

Machine Learning using marginalized graph kernel for chemical molecules.