A collection of important graph embedding, classification and representation learning papers with implementations.
翻译 - 一系列重要的图形嵌入,分类和表示学习论文以及实现。
#计算机科学#Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
翻译 - Alink是阿里巴巴计算平台的PAI团队开发的基于Flink的机器学习算法平台。
#计算机科学#A distributed graph deep learning framework.
翻译 - 分布式图深度学习框架。
#计算机科学#PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
翻译 - PyTorch Geometric的时间扩展库
#计算机科学#Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
翻译 - 通用社区检测和网络嵌入库,用于基于NetworkX构建的研究。
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
翻译 - CogDL:用于图形的广泛研究工具包
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
#计算机科学#A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
翻译 - “ graph2vec:学习图的分布式表示形式”(MLGWorkshop 2017)的并行实现。
#计算机科学#Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
#计算机科学#A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
翻译 - PyTorch实现的“ SimGNN:一种用于快速图相似度计算的神经网络方法”(WSDM 2019)。
#计算机科学#Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
#计算机科学#A repository of pretty cool datasets that I collected for network science and machine learning research.
#计算机科学#Minimum-distortion embedding with PyTorch
#计算机科学#Deep and conventional community detection related papers, implementations, datasets, and tools.
Recommender Systems Paperlist that I am interested in
#计算机科学#Representation-Learning-on-Heterogeneous-Graph
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
#计算机科学#A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
Representation learning on dynamic graphs using self-attention networks