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m-nmf

An implementation of "Community Preserving Network Embedding" (AAAI 2017)

magnn

Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

matplotlib

matplotlib: plotting with Python

metapath2vec

metapath2vec-1

metapath2vec: Scalable Representation Learning for Heterogeneous Networks(KDD 2017) in Tensorflow

mge

implementation of MGE: Multi-view Graph Contrastive Encoding for Graph Neural Networks Pre-training

mixed-membership-stochastic-blockmodel

Mixed Membership Stochastic Blockmodel Implementation with 3 Inference Schemes

mlcompetition

机器学习竞赛代码

models

multimetapath2vec

Modification of metapath2vec algorithm for representation learning

narm

The code for the paper "Leveraging Node Attributes for Incomplete Relational Data" in ICML 2017

nebula

A distributed, fast open-source graph database featuring horizontal scalability and high availability

netclus

Implementation of NetClus:Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema

netflix

netmf

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

nettack

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

networkx

Official NetworkX source code repository.

neural_graph_collaborative_filtering

Neural Graph Collaborative Filtering, SIGIR2019

node_embedding_attack

Adversarial Attacks on Node Embeddings via Graph Poisoning

nrl-implement

Implementation of some network representation methods including DeepWalk, LINE, node2vec and GraphGAN

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

ny-airbnb-analysis

Build an interactive web app with streamlit, plotly and folium

openfl

An open framework for Federated Learning.

openne

An Open-Source Package for Network Embedding (NE)

overlapping-community-detection

Implementation of "Overlapping Community Detection with Graph Neural Networks"

pac-proj1

Structure-based Social Network De-anonymization Attack

pace2017

A step-by-step Keras implementation of PACE (Preference And Context Embedding) described in our KDD 2017 paper.

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

powerlaw

This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region.

powerlaw-1