# CANE

**Repository Path**: thunlp/CANE

## Basic Information

- **Project Name**: CANE
- **Description**: Source code and datasets of "CANE: Context-Aware Network  Embedding for Relation Modeling"
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-29
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CANE
Source code and datasets of ACL2017 paper: "CANE: Context-Aware Network  Embedding for Relation Modeling"

## Datasets
This folder "datasets" contains three datasets used in CANE, including Cora, HepTh and Zhihu. In each dataset, there are two files named "data.txt" and "graph.txt".

* data.txt: Each line represents the text information of a vertex.    
* graph.txt: The edgelist file of current social network.

Besides, there is an additional "group.txt" file in Cora.

* group.txt: Each vertex in Cora has been annotated with a label. This file can be used for vertex classification.

## Run
Run the following command for training CANE:

    python3 run.py --dataset [cora,HepTh,zhihu] --gpu gpu_id --ratio [0.15,0.25,...] --rho rho_value

For example, you can train like:

    python3 run.py --dataset zhihu --gpu 0 --ratio 0.55 --rho 1.0,0.3,0.3

## Experimental Results

The experimental results are generated by the newest version of codes:

|       | 0.15 | 0.25 | 0.35 | 0.45 | 0.55 | 0.65 | 0.75 | 0.85 | 0.95 |
| ----- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| cora  | 85.2 | 90.5 | 92.2 | 93.5 | 93.4 | 93.6 | 94.4 | 95   | 92.5 |
| HepTh | 85   | 89.7 | 91.7 | 95   | 94.4 | 94.2 | 95.1 | 95.8 | 93.1 |
| zhihu | 64.5 | 67.1 | 69.2 | 69.9 | 72   | 72.2 | 72.5 | 72.8 | 73.3 |

## Dependencies

* Tensorflow == 1.11.0
* Scipy == 1.1.0
* Numpy == 1.16.2

## Cite
If you use the code, please cite this paper:

_Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun. CANE: Context-Aware Network  Embedding for Relation Modeling. The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)._

For more related works on network representation learning, please refer to my [homepage](http://thunlp.org/~tcc/).