{"id":32807092,"url":"https://github.com/alanmc123/github-socialnetwork","last_synced_at":"2026-04-15T11:37:19.829Z","repository":{"id":321068732,"uuid":"1084319074","full_name":"AlanMC123/GitHub-SocialNetwork","owner":"AlanMC123","description":"Homework: Analysis on the GitHub Social Connection","archived":false,"fork":false,"pushed_at":"2025-11-03T16:17:14.000Z","size":6295,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-11-03T18:25:37.801Z","etag":null,"topics":["github","social-network","social-network-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AlanMC123.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-27T14:20:54.000Z","updated_at":"2025-11-03T16:17:17.000Z","dependencies_parsed_at":"2025-10-27T17:13:34.315Z","dependency_job_id":"4b0530c7-a2f4-4735-92c7-55f141f15430","html_url":"https://github.com/AlanMC123/GitHub-SocialNetwork","commit_stats":null,"previous_names":["alanmc123/github-socialnetwork"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AlanMC123/GitHub-SocialNetwork","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlanMC123%2FGitHub-SocialNetwork","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlanMC123%2FGitHub-SocialNetwork/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlanMC123%2FGitHub-SocialNetwork/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlanMC123%2FGitHub-SocialNetwork/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AlanMC123","download_url":"https://codeload.github.com/AlanMC123/GitHub-SocialNetwork/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AlanMC123%2FGitHub-SocialNetwork/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":283027924,"owners_count":26767085,"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","status":"online","status_checked_at":"2025-11-06T02:00:06.180Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["github","social-network","social-network-analysis"],"created_at":"2025-11-06T15:01:15.462Z","updated_at":"2026-04-15T11:37:19.807Z","avatar_url":"https://github.com/AlanMC123.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GitHub-SocialNetwork\n开放的数据集，用于分析GitHub的社交网络，重点研究Web开发者与机器学习开发者的网络结构差异。\n\n## 项目概述\n本项目对GitHub社交网络进行了全面的分析，包括网络结构特征、节点级指标、社区结构、链接预测、节点分类和影响传播等方面。通过多种分析方法，揭示了不同类型开发者在网络中的特征和差异，为理解开源社区的结构和演化提供了深入的洞察。\n\n## 目录结构\n\n```\nGitHub-SocialNetwork/\n├── analysis/              # 分析脚本和结果目录\n│   ├── initial_analysis/         # 初始分析\n│   │   ├── primary_analysis.py   # 初始分析代码\n│   │   └── outputs/              # 初始分析结果\n│   ├── structure_analysis/       # 网络结构分析\n│   │   ├── structure_analysis.py # 结构分析代码\n│   │   └── outputs/              # 结构分析结果\n│   ├── node_level/               # 节点级分析\n│   │   ├── node_level_analysis.py # 节点级分析代码\n│   │   └── outputs/              # 节点级分析结果\n│   ├── ergm_analysis/            # ERGM模型分析\n│   │   ├── ergm_graphtool_analysis.py # ERGM分析代码\n│   │   └── outputs/              # ERGM分析结果\n│   ├── gnn_analysis/             # GNN网络分析\n│   │   ├── gnn_network_analysis.py # GNN分析代码\n│   │   └── outputs/              # GNN分析结果\n│   ├── community_analysis/       # 社区分析\n│   │   ├── visualize_communities.py # 社区可视化代码\n│   │   ├── visualize_node_clusters.py # 聚类可视化代码\n│   │   └── outputs/              # 社区分析结果\n│   ├── link_prediction/          # 链接预测研究\n│   │   ├── link_prediction.py    # 链接预测代码\n│   │   └── outputs/              # 链接预测结果\n│   ├── node_classification/      # 节点分类研究\n│   │   ├── node_classification.py # 节点分类代码\n│   │   └── outputs/              # 节点分类结果\n│   └── influence_propagation/    # 影响传播研究\n│       ├── influence_propagation.py # 影响传播代码\n│       └── outputs/              # 影响传播结果\n├── data/                 # 原始数据集\n│   ├── musae_git_edges.csv         # 边数据\n│   ├── musae_git_edges_fixed.csv   # 修复后的边数据\n│   ├── musae_git_features.json     # 节点特征\n│   ├── musae_git_target.csv         # 节点标签\n│   └── dataset-README.txt          # 数据集说明\n├── README.md             # 项目说明\n├── research_guide.md     # 研究指南\n└── 项目说明文档.md         # 中文项目说明\n```\n\n## 数据说明\n\n### 节点数据 `musae_git_target.csv`\n包含GitHub开发者节点信息，每行代表一个开发者：\n- `id`：开发者唯一标识符\n- `name`：GitHub用户名\n- `ml_target`：开发者类型（0 = Web开发者，1 = 机器学习开发者）\n\n### 边数据 `musae_git_edges_fixed.csv`\n包含开发者之间的关注关系：\n- `source`：关注者ID\n- `target`：被关注者ID\n\n### 特征数据 `musae_git_features.json`\n包含每个节点的256维二进制特征向量。\n\n## 安装依赖\n\n### 基本依赖\n```bash\npip install pandas numpy networkx matplotlib seaborn tqdm scikit-learn scipy igraph reportlab\n```\n\n### GNN相关依赖\n对于链接预测和节点分类脚本，还需要安装PyTorch和PyTorch Geometric：\n```bash\npip install torch torch_geometric\n```\n\n## 研究方向\n\n### 1. 初始分析\n- **内容**：分析Web开发者与机器学习开发者的网络结构差异\n- **方法**：计算中心性指标（度中心性、介数中心性、接近中心性、特征向量中心性）\n- **结果**：生成中心性指标可视化和网络分析报告\n\n### 2. 网络结构分析\n- **内容**：分析GitHub社交网络的结构特征\n- **方法**：计算度分布、密度、聚类系数、平均路径长度等\n- **结果**：生成度分布图表和网络结构分析报告\n\n### 3. 节点级分析\n- **内容**：计算节点级指标\n- **方法**：计算PageRank、K-core（核数）和结构洞指标\n- **结果**：生成节点级指标数据集\n\n### 4. ERGM模型分析\n- **内容**：使用ERGM模型拟合GitHub社交网络\n- **方法**：拟合指数随机图模型\n- **结果**：生成度分布和边共享伙伴分布图表\n\n### 5. GNN网络分析\n- **内容**：使用图神经网络进行网络分析\n- **方法**：训练GNN模型生成节点嵌入，进行聚类分析\n- **结果**：生成聚类结果和社区检测结果\n\n### 6. 社区分析\n- **内容**：检测和分析GitHub中的开发者社区\n- **方法**：使用Louvain算法和K-means聚类\n- **结果**：生成社区可视化图表和统计信息\n\n### 7. 链接预测\n- **内容**：基于现有网络结构预测未来可能形成的连接\n- **方法**：实现5种基于相似度的方法和3种GNN方法\n- **结果**：生成方法性能比较和预测链接分析\n\n### 8. 节点分类\n- **内容**：利用GNN嵌入和节点特征进行更精确的节点分类\n- **方法**：比较GCN、GraphSAGE、GAT等不同GNN模型\n- **结果**：生成模型性能比较和特征重要性分析\n\n### 9. 影响传播\n- **内容**：模拟信息在GitHub网络中的传播过程\n- **方法**：实现SIR和IC传播模型\n- **结果**：生成传播过程可视化和关键节点分析\n\n## 使用方法\n\n### 运行单个分析脚本\n```bash\n# 运行初始分析\npython analysis/initial_analysis/primary_analysis.py\n\n# 运行链接预测\npython analysis/link_prediction/link_prediction.py\n\n# 运行节点分类\npython analysis/node_classification/node_classification.py\n\n# 运行影响传播模型\npython analysis/influence_propagation/influence_propagation.py\n```\n\n### 运行完整分析\n参考 `research_guide.md` 文件获取详细的运行说明和参数设置。\n\n## 结果说明\n\n所有分析结果保存在对应分析目录的 `outputs` 文件夹中，包括：\n- CSV文件：包含模型性能指标和分析结果\n- PNG文件：包含可视化图表\n- PDF文件：包含详细的分析报告\n\n## 项目特点\n\n1. **多方法支持**：实现了多种网络分析方法，包括传统方法和深度学习方法\n2. **全面的可视化**：生成多种类型的可视化图表，便于结果理解\n3. **模块化设计**：各分析脚本功能明确，便于扩展和修改\n4. **详细的文档**：提供完整的数据集说明和分析结果\n5. **可复现性**：所有分析代码和数据均可复现\n\n## 应用场景\n\n1. **社交网络分析**：研究GitHub开发者之间的社交关系\n2. **开发者分类**：比较Web开发者与机器学习开发者的网络行为差异\n3. **社区检测**：发现GitHub中的开发者社区\n4. **网络结构研究**：分析开源社区的网络结构特征\n5. **预测模型**：基于网络结构预测开发者类型和未来连接\n6. **信息传播研究**：模拟开源项目信息的传播过程\n\n## 许可证\n\n本项目采用MIT许可证，详见LICENSE文件。","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falanmc123%2Fgithub-socialnetwork","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falanmc123%2Fgithub-socialnetwork","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falanmc123%2Fgithub-socialnetwork/lists"}