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ggnn.tensorflow

Tensorflow implementation of Gated Graph Neural Network for Graph Classification

ggnn_reasoning

PyTorch implementation for Graph Gated Neural Network (for Knowledge Graphs)

gmnn

gnnpapers

Must-read papers on graph neural networks (GNN)

grami

GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.

graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

graph-learning

Graph model implementation

graph-neural-net

Graph Convolutional Networks, Graph Attention Networks, Gated Graph Neural Net

graph-neural-network

All materials related tp GNN

graph-neural-network-review

graph-neural-networks-for-drug-discovery

graphgallery

GraphGallery is a gallery of state-of-the-art Graph Neural Networks (GNNs) for TensorFlow 2.x and PyTorch.

halp

Hypergraph Algorithms Package

han

Network Embedding

hin-dataset

Heterogeneous Information Network Datasets

hypergraphs

Hypergraphs

hypernetx

Python package for hypergraph analysis and visualization.

iclr2022-openreviewdata

ICLR 2022 Paper submission trend analysis from https://openreview.net/group?id=ICLR.cc/2022/Conference

javaguide

【Java学习+面试指南】 一份涵盖大部分Java程序员所需要掌握的核心知识。

jaxnet

Alternative to TensorFlow2/Keras/PyTorch for more concise, robust and optimized deep learning code

jd_spider

两只蠢萌京东的分布式爬虫.

jodie

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

kahypar

KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

kdd2019_hetgnn

code of HetGNN

kge

LibKGE - A knowledge graph embedding library for reproducible research

knowledge_graph_attention_network

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

lanczosnetwork

Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019

linkpred

A High Performance Library for Link Prediction in Complex Networks

matplotlib-multilayer-network

small template code to create a multilayer network using matplotlib and networkx

mgcnn_alkaloid

Molecular Graph Convolutional Neural Networks