SauravMaheshkar/NDC-classes

---
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="NDC-classes", 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}
}
```

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: 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"

• 配置名称: 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"

• 配置名称: 原始

  • 数据文件: "raw/*.txt"