• TransHawkes
• Framework
• • DataSet
  • Steps to run TransHawkes
  • Dependencies
  • Acknowledge

TransHawkes

TransHwakes: Prediction of Social Media Content Popularity Based on Transfomer Integrated Hawkes Process
It is an algorithm for measuring the influence of social media advertisers' advertising posts

Framework

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:

<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 TransHawkes

1.split the data to train set, validation set and test set.
command:

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

2.trainsform the datasets to the format of ".pkl"
command:

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

3.train TransHawkes
command:

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

The pre trained model can be obtained from the following link:
URL: https://pan.baidu.com/s/1yFRhs-wMPtm9LzEIsEalVQ
Password: q5A5

Dependencies

The script has been tested running under Python 3.5.2, with the following packages installed (along with their dependencies):

• numpy==1.14.1
• scipy==1.0.0
• networkx==2.1
• tensorflow-gpu==1.6.0

In addition, CUDA 9.0 and cuDNN 7 have been used.

Acknowledge

This work was supported by the National Key R&D Program of China under Grant No. 2020AAA0103804 and partially supported by grants from the National Natural Science Foundation of China (No.72004021). This work belongs to the University of science and technology of China.