{"id":15659665,"url":"https://github.com/ysh329/link-prediction","last_synced_at":"2025-05-05T19:35:16.481Z","repository":{"id":83527155,"uuid":"47450104","full_name":"ysh329/link-prediction","owner":"ysh329","description":"[UNMAINTAINED] 基于PySpark与MySQL的复杂网络链路预测。","archived":false,"fork":false,"pushed_at":"2018-01-22T02:58:38.000Z","size":39,"stargazers_count":22,"open_issues_count":0,"forks_count":7,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-30T23:05:30.439Z","etag":null,"topics":["link-prediction","network","pyspark","spark"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ysh329.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2015-12-05T09:29:30.000Z","updated_at":"2024-11-06T05:32:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"77871a93-421e-4603-b308-dbb2d0619044","html_url":"https://github.com/ysh329/link-prediction","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Flink-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Flink-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Flink-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Flink-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ysh329","download_url":"https://codeload.github.com/ysh329/link-prediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252563149,"owners_count":21768411,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["link-prediction","network","pyspark","spark"],"created_at":"2024-10-03T13:18:10.042Z","updated_at":"2025-05-05T19:35:16.463Z","avatar_url":"https://github.com/ysh329.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# link-prediction\n基于PySpark和MySQL实现复杂网络的（错误）链路预测。结合PySpark的RDD操作实现图的出入度、某两个节点的共同邻居等的计算。\n\nSpark配置：\n\n* scala-2.11.5\n* spark-1.2.0\n* jdk1.8.0_31\n\n虽然现在Spark版本迭代更新很快，但对于初学PySpark的同学，本项目仍有一定参考价值。\n\n参考论文:\n\n* 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5):651-661.\n\n\u003e相比NetworkX，结合Spark的图计算能更快一些，由于PySpark里没有GraphX模块，所以基于RDD的基本操作算子，自己实现一些图的基本计算。\n\n## Data\n6个CSV文件，对应6个复杂网络（三种网络：Internet网络、生物信息网络、社交媒体网络，每种网络有两个。即总共3个有向图网络，3个无向图网络），数据存储的形式是边集数组。\n* 对于有向图来说，每行数据有两个元素表示一条有向边arc。第一个元素是该边的起始节点，第二个元素是该边的终点；\n* 对于无向图来说，每行数据也是两个元素，代表组成该无向边edge的两个节点。\n代码有一段是将数据从CSV文件中读取并保存在MySQL中。\n\n\n## Algorithm\n方法记录见[个人博客](http://yuenshome.cn)之[《复杂网络链路预测》](http://yuenshome.cn/?p=3753)。这篇笔记记录是对该文的学习总结，本项目主要实现了基于相似性的链路预测部分的算法。\n\n参考论文:\n\n* 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5):651-661.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fysh329%2Flink-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fysh329%2Flink-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fysh329%2Flink-prediction/lists"}