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

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.

Classification Task on Graphs using Graph Neural Networks and Graph Kernels - Thesis Project

This project aims to compare the performance obtained using a linear Support Vector Machine model whose data was first processed through a Shortest Path kernel with the same SVM, this time with data also processed by two alternative Manifold Learning techniques: Isomap and Spectral Embedding.

Implementation of Deep Divergence Event Graph Kernels

A Julia package for kernel functions on graphs

Semantics aware quality evaluation of building 3D models: a learning approach

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

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

A package for downloading and working with graph datasets

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

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

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

A collection of graph classification methods

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"

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

Deriving Neural Architectures from Sequence and Graph Kernels