SauravMaheshkar/tags-ask-ubuntu
收藏资源简介：
--- license: unknown task_categories: - graph-ml tags: - chemistry configs: - config_name: transductive data_files: - split: train path: "processed/transductive/train_df.csv" - split: valid path: "processed/transductive/val_df.csv" - split: test path: "processed/transductive/test_df.csv" - config_name: inductive data_files: - split: train path: "processed/inductive/train_df.csv" - split: valid path: "processed/inductive/val_df.csv" - split: test path: "processed/inductive/test_df.csv" - config_name: raw data_files: "raw/*.txt" --- Source Paper: https://arxiv.org/abs/1802.06916 ### Usage ``` from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="tags-ask-ubuntu", split="train") ``` ### Citation ```misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} } ```
--- 许可证：未知 任务类别： - 图机器学习（graph-ml） 标签： - 化学 配置： - 配置名称：直推式（transductive） 数据文件： - 拆分集：训练集（train） 路径: "processed/transductive/train_df.csv" - 拆分集：验证集（valid） 路径: "processed/transductive/val_df.csv" - 拆分集：测试集（test） 路径: "processed/transductive/test_df.csv" - 配置名称：归纳式（inductive） 数据文件： - 拆分集：训练集（train） 路径: "processed/inductive/train_df.csv" - 拆分集：验证集（valid） 路径: "processed/inductive/val_df.csv" - 拆分集：测试集（test） 路径: "processed/inductive/test_df.csv" - 配置名称：原始（raw） 数据文件: "raw/*.txt" --- 来源论文：https://arxiv.org/abs/1802.06916 ### 使用示例 from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="tags-ask-ubuntu", split="train") ### 引用格式 misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} }
数据集概述
许可证
- 许可证: 未知
任务类别
- 图机器学习
标签
- 化学
配置详情
配置一: 直推式
- 训练数据文件:
processed/transductive/train_df.csv - 验证数据文件:
processed/transductive/val_df.csv - 测试数据文件:
processed/transductive/test_df.csv
配置二: 归纳式
- 训练数据文件:
processed/inductive/train_df.csv - 验证数据文件:
processed/inductive/val_df.csv - 测试数据文件:
processed/inductive/test_df.csv
配置三: 原始数据
- 数据文件:
raw/*.txt
