SauravMaheshkar/threads-ask-ubuntu

--- 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="threads-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（Chemistry） 配置项： - 配置名称：直推式（Transductive） 数据文件： - 拆分：训练集，路径："processed/transductive/train_df.csv" - 拆分：验证集，路径："processed/transductive/val_df.csv" - 拆分：测试集，路径："processed/transductive/test_df.csv" - 配置名称：归纳式（Inductive） 数据文件： - 拆分：训练集，路径："processed/inductive/train_df.csv" - 拆分：验证集，路径："processed/inductive/val_df.csv" - 拆分：测试集，路径："processed/inductive/test_df.csv" - 配置名称：原始版（Raw） 数据文件："raw/*.txt" ### 来源论文 https://arxiv.org/abs/1802.06916 ### 使用方法 from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="threads-ask-ubuntu", split="train") ### 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} }

原始信息汇总

数据集概述

许可

• 许可证: 未知

任务类别

• 图机器学习

标签

• 化学

配置

• 配置名称: transductive

  • 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"

• 配置名称: inductive

  • 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"

• 配置名称: raw

  • 原始数据文件: "raw/*.txt"