SauravMaheshkar/congress-bills-25

--- 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="congress-bills-25", 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（chemistry） 配置项： - 配置名称：直推式（transductive） 数据文件： - 拆分集：训练集，路径："processed/transductive/train_df.csv" - 拆分集：验证集，路径："processed/transductive/val_df.csv" - 拆分集：测试集，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，路径："processed/inductive/train_df.csv" - 拆分集：验证集，路径："processed/inductive/val_df.csv" - 拆分集：测试集，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 ### 用法 from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="congress-bills-25", split="train") ### 引用 misc @article{Benson-2018-simplicial, 作者 = {Benson, Austin R. 与 Abebe, Rediet 与 Schaub, Michael T. 与 Jadbabaie, Ali 与 Kleinberg, Jon}, 标题 = {单纯形闭包与高阶链路预测（Simplicial closure and higher-order link prediction）}, 年份 = {2018}, DOI = {10.1073/pnas.1800683115}, 出版商 = {美国国家科学院（National Academy of Sciences）}, ISSN = {0027-8424}, 期刊 = {《美国国家科学院院刊》（Proceedings of the National Academy of Sciences）} }

原始信息汇总

数据集概述

许可证

• 许可证类型：未知

任务类别

• 图机器学习

标签

• 化学

配置

• transductive
  • 训练集路径：processed/transductive/train_df.csv
  • 验证集路径：processed/transductive/val_df.csv
  • 测试集路径：processed/transductive/test_df.csv
• inductive
  • 训练集路径：processed/inductive/train_df.csv
  • 验证集路径：processed/inductive/val_df.csv
  • 测试集路径：processed/inductive/test_df.csv
• raw
  • 数据文件路径：raw/*.txt

引用

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} }