# DeepHawkes_py3.6

**Repository Path**: wu58814/DeepHawkes_py3.6

## Basic Information

- **Project Name**: DeepHawkes_py3.6
- **Description**: Amazing DeepHawkes
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-27
- **Last Updated**: 2022-11-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepHawkes_py3.6

Rewirte "DeepHawkes" for python 3.6 edition.
The dataset is selected partly from CaoQi's.

Origin: <https://github.com/CaoQi92/DeepHawkes>

----

This repository is an implementation of our proposed DeepHawkes model in the following paper:

```bib
Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng. 2017. DeepHawkes: Bridging the Gap between 
Prediction and Understanding of Information Cascades. In Proceedings of CIKM'17, Singapore., November 
6-10, 2017, 11 pages.
```

For more details, you can download this paper from ACM DIGITAL LIBRARY.
The following url is the corresponding download link:
<https://dl.acm.org/citation.cfm?id=3132973&CFID=1005695721&CFTOKEN=57128415>

## DataSet

We publish the Sina Weibo Dataset used in our paper,i.e., dataset_weibo.txt. It contains 119,313 messages in June 1, 2016.
Each line contains the information of a certain message, the format of which is:

```text
<message_id>\tab<user_id>\tab<publish_time>\tab<retweet_number>\tab<retweets>
<message_id>:     the unique id of each message, ranging from 1 to 119,313.
<root_user_id>:   the unique id of root user. The user id ranges from 1 to 6,738,040.
<publish_time>:   the publish time of this message, recorded as unix timestamp.
<retweet_number>: the total number of retweets of this message within 24 hours.
<retweets>:       the retweets of this message, each retweet is split by " ". Within each retweet, it records 
the entile path for this retweet, the format of which is <user1>/<user2>/......<user n>:<retweet_time>.
```

This dataset is limited to only use in research. And when you use this dataset, please cite our paper as listed above.

Downlowd link: <https://pan.baidu.com/s/1c2rnvJq>

password: ijp6

## Steps to run DeepHawkes

1. Installation requirements in `environment.yml`.

    ```shell
    conda env create -f environment.yml
    conda activate tf1.6
    ```

2. Set the parameters of dataset and observation in `deep_learning/config.py` and `gen_sequence/config.py` quickly.

    ```shell
    python init_config.py --observation 1.0 --dataset twitter
    ```

3. split the data to train set, validation set and test set.

    ```shell
    cd gen_sequence
    python gen_shortestpath.py
    #you can configure parameters and filepath in the file of "config.py"
     ```

4. trainsform the datasets to the format of ".pkl"

    ```shell
    cd deep_learning
    python preprocess.py
    #you can configure parameters and filepath in the file of "config.py"
     ```

5. train DeepHawkes

    ```shell
    cd deep_learning
    python run_sparse.py learning_rate learning_rate_for_embeddings l2 dropout
    #exsamples  python -u run_sparse.py 0.005 0.0005 0.05 0.8 > output.log$(date "+%Y-%m-%d %H:%M:%S")
    ```