Named Entity Recognition as Dependency Parsing

Named Entity Recognition as Dependency Parsing

GraphVite: A General and High-performance Graph Embedding System

「Java面试小抄」一份通向理想互联网公司的面试汇总，包括 Java基础、Java并发、JVM、MySQL、Redis、Spring、MyBatis、Kafka、计算机操作系统、计算机网络、系统设计、分布式、Java 项目实战等

ElasticSearch入门教程

Postgres to Elasticsearch/OpenSearch sync

Pytorch implementation of paper 'GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks' to appear on WSDM2021

Graph Neural Network Library for PyTorch

Code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" (NeurIPS 20)

😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库

This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 2020

程序员在家做饭方法指南。Programmer's guide about how to cook at home (Simplified Chinese only).

Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

Awesome Incremental Learning

A distributed transaction framework, supports workflow, saga, tcc, xa, 2-phase message, outbox patterns, supports many languages.

go-chat.使用Go基于WebSocket开发的web聊天应用。单聊，群聊。文字，图片，语音，视频消息，屏幕共享，剪切板图片，基于WebRTC的P2P语音通话，视频聊天。

A collection of important graph embedding, classification and representation learning papers with implementations.

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

Spark related libraries and tools

The implementation of TUA (Targeted Universal Attack)

A curated list of adversarial attacks and defenses papers on graph-structured data.

🔥 经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

Must-read Papers on Textual Adversarial Attack and Defense

A curated collection of adversarial attack and defense on graph data.

A curated list for awesome self-supervised learning for graphs.

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"