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  • 👋 Hi, I’m @zhouaimin
  • 👀 I’m interested in graph and algorithms

快乐的咸菜饼's Projects

agriculture_knowledgegraph icon agriculture_knowledgegraph

农业知识图谱(AgriKG):农业领域的信息检索,命名实体识别,关系抽取,智能问答,辅助决策

al-folio icon al-folio

A beautiful, simple, clean, and responsive Jekyll theme for academics

arcgis-pro-sdk icon arcgis-pro-sdk

ArcGIS Pro SDK for Microsoft .NET is the new .NET SDK for the ArcGIS Pro Application.

awesome-vue icon awesome-vue

🎉 A curated list of awesome things related to Vue.js

blindwatermark icon blindwatermark

使用盲水印保护创作者的知识产权using invisible watermark to protect creator's intellectual property

campus_recruitmen_questions icon campus_recruitmen_questions

2021年最新整理,5000道秋招/提前批/春招/常用面试题(含答案),包括leetcode,校招笔试题,面试题,算法题,语法题。持续更新中

communities icon communities

Library of community detection algorithms and visualization tools

corpus icon corpus

自然语言处理,知识图谱相关语料。按照Task细分,欢迎PR。

cs-notes icon cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

daily-interview icon daily-interview

Datawhale成员整理的面经,内容包括机器学习,CV,NLP,推荐,开发等,欢迎大家star

deepqa icon deepqa

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

fucking-algorithm icon fucking-algorithm

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

geopy icon geopy

Geocoding library for Python.

glsearch icon glsearch

GLSearch: Maximum Common Subgraph Detection via Learning to Search

gstore icon gstore

gStore - a graph based RDF triple store.

hithesis icon hithesis

嗨!thesis!哈尔滨工业大学毕业论文LaTeX模板

javaguide icon 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。准备 Java 面试,首选 JavaGuide!

mcs icon mcs

Finding maximum common subgraph and minimum common supergraph of two graphs

multi-query-subgraph-matching icon multi-query-subgraph-matching

Implemented Multi-query sub graph isomorphism search algorithm to optimize general subgraph matching performed on graph databases such as Neo4j or Cassandra. The algorithm creates new queries by identifying common graphs in multiple queries and extract and store the result in the cache to avoid the redundant search for the same common graph. The optimized algorithm reduced the search time by 20% on average on iHuman and iProtein graph dataset stored in Neo4j graph database.

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