Social network analysis using deep learning: applications and schemes

被引：20
作者：
Abbas, Ash Mohammad [1 ]
机构：
[1] Aligarh Muslim Univ, Zakir Husain Coll Engn & Technol, Dept Comp Engn, Aligarh, Uttar Pradesh, India
来源：
SOCIAL NETWORK ANALYSIS AND MINING | 2021年 / 11卷 / 01期
关键词：
Social network analysis; Deep learning; Sentiment analysis; Opinion analysis; Recommender systems; Community detection; Fake news detection; NEURAL-NETWORKS; CLASSIFICATION; TWITTER;
D O I：
10.1007/s13278-021-00799-z
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online social networks (OSNs) are part of daily life of human beings. Millions of users are connected through online social networks. Due to very large number of users and huge amount of data, social network analysis is a challenging task. The emergence of deep learning techniques has enabled to carry out a rigorous analysis of OSNs. A lot of research is carried out in the area of social network analysis using deep learning techniques from different perspectives. In this paper, we provide an overview of state-of-the-art research for different applications of social network analysis using deep learning techniques. We consider applications such as opinion analysis, sentiment analysis, text classification, recommender systems, structural analysis, anomaly detection, and fake news detection. We compare different schemes on the basis of their focus and features. Further, we point out directions for future work.
引用
收藏
页数：21
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