.png){kind=icon}
返回
Topic Propagation Prediction Based on Dynamic Probability Model
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2914479.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As social networks play an increasingly important role in people's lives, people are more likely to discuss hot topics on social networks. Predicting the spread of hot topics, known as topic propagation prediction is an important task. Due to the unpredictability of the users and topics in social networks, predicting the topic propagation trend is still a major challenge. Different users play different roles in topic propagation. However, existing studies have not utilized user role analysis. In this paper, we propose a topic propagation prediction method (TPP) based on user role analysis and dynamic probability model. First, we describe our user role analysis, which incorporates four user-factors to characterize user attributes along two dimensions. Second, we combine dynamic probability model with user role analysis to accurately predict the topic propagation trend. Finally, we prove the efficiency of TPP by experiments.
Keyword:
Probability model
user role analysis
topic propagation prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏分享收藏Induction of immune tolerance and suppression of anaphylaxis in a child with haemophilia B by simple plasmapheresis and antigen exposure: progress report通过单纯血浆置换和抗原暴露诱导免疫耐受并抑制过敏性休克在一名血友病B患儿中的进展报告.png)
Haemophilia
HaemophiliaIF0
PREAI
分享收藏
查看更多内容