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