A distributed graph deep learning framework.

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Graph Sampling is a python package containing various approaches which samples the original graph according to different sample sizes.

A general-purpose, distributed graph random walk engine.

Applied Probability Theory for Everyone

Robot path planning, mapping and exploration algorithms

Dataset and code for our paper "Replicate, Walk, and Stop on Syntax: an Effective Neural Network Model for Aspect-Level Sentiment Classification" (AAAI'20)

From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)

Outlier detection for categorical data

It provides some typical graph embedding techniques based on task-free or task-specific intuitions.

Personalized PageRank (PPR) on GraphLab PowerGraph

Website built using React Framework for visualizing Pathfinding and Maze Generation Algorithms.

Different Implementations of 2D Procedural Maps

New Algorithms for Learning on Hypergraphs

An implementation of the absorbing random-walk centrality

Random walk to calculate the tortuosity tensor of images

DISCOTRESS is a software package to simulate and analyse the dynamics on arbitrary Markov chains

Analyzing Stock Movements using Markov Chains and Monte Carlo Simulation

Reinforcement Learning - Implementation of Exercises, algorithms from the book Sutton Barto and David silver's RL course in Python, OpenAI Gym.

For shallow-water Lagrangian particle routing.

A python package for constructing and analysing minimum spanning trees.

Attributed Biased Random Walks (ABRW) is an Attributed Network Embedding method

Reluctant walks tend to exit the quarter-plane in which they are constrained (because of strong negative drift), but this work shows how to randomly sample large reluctant walks anyway. (Lumbroso, Mishna, Ponty, 2017)

2D and 3D wormlike chain generator for Python and written in C++

Implementation of the Paper "Entity Linking in Web Tables with Multiple Linked Knowledge Bases"

Stochastic SIR models; adding age-structures and social contact data for the spread of covid-19. Lattice model for identifying and isolating hotspots. This has been further developed into a network(graph) of multiple clusters(lattices) and tracing the infection in such a population.

Spring 2018 Monte Carlo project at ENSAE: simulation of self-avoiding random walks

Undergraduate graduation project (Entity Linking System in Web Tables with Multiple Linked Knowledge Bases) at SEU.

Source Code for IJCAI 2018 paper "Biased Random Walk based Social Regularization for Word Embeddings"