@inproceedings{f954aa3d75574503baac96fad935eba4,
title = "A modified community-level diffusion extraction in social network",
abstract = "Equipped with more convenient facilities and features, online social networks have become the most popular platform for people's communication. It is increasingly important to model information propagation in such networks. Most of the state-of-the-art algorithms of information diffusion model focus on individual-level diffusion and does not consider the impact of social relations on user's expression, making them either unable to uncover diffusion patterns accurately or unable to capture dynamically changing topics of text stream in social networks. To address these issues, we proposed a dynamic community-level diffusion model (DCDM) in this paper to capture diffusion patterns based on coordinated dynamic semantic analysis by multiple topic-word distribution and structure analysis. Comparative experiments are conducted on the real dataset from Tweet. Experimental results show our diffusion model outperforms the state-of-the-art methods.",
keywords = "Communities, Information diffusion, Social networks",
author = "Huajian Chang and Hong Shen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019 ; Conference date: 05-12-2019 Through 07-12-2019",
year = "2019",
month = dec,
doi = "10.1109/PDCAT46702.2019.00101",
language = "English",
series = "Proceedings - 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "509--512",
editor = "Hui Tian and Hong Shen and Tan, {Wee Lum}",
booktitle = "Proceedings - 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019",
address = "United States",
}