Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

A Fast Prediction Algorithm for Sina Weibo Users with Time Correlation Cognition

Authors
Xixu He, Leiting Chen, Min Zhang
Corresponding Author
Xixu He
Available Online October 2016.
DOI
https://doi.org/10.2991/ceie-16.2017.6How to use a DOI?
Keywords
Time Related Cognition; Classification Prediction; Granular Computing
Abstract
Social network is the most important way of information exchange and communication at present, which is a key step to study users' behavior in social network. Current methods for users 'behavior classification are more diverse, but it is difficult to assess the impact of specific events in different time. In this paper, the rapid predicted classification algorithm based on correlation time cognition for weibo users in specific events is proposed, which can accomplish weibo user classification according to their behavior in the short time window, so as to establish a more accurate users behavior relation network. Finally, through the experiments, it is shown that the proposed algorithm offers more powerful and robust performance than competing algorithms.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-312-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/ceie-16.2017.6How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xixu He
AU  - Leiting Chen
AU  - Min Zhang
PY  - 2016/10
DA  - 2016/10
TI  - A Fast Prediction Algorithm for Sina Weibo Users with Time Correlation Cognition
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 43
EP  - 51
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.6
DO  - https://doi.org/10.2991/ceie-16.2017.6
ID  - He2016/10
ER  -