New research methods & algorithms in social network analysis

被引：22

作者：

Camacho, David ^{[1
]}

Victoria Luzon, Ma ^{[2
]}

Cambria, Erik ^{[3
]}

机构：

[1] Univ Politecn Madrid, Dept Sistemas Informat, Calle Alan Turing S-N, Madrid 28031, Spain

[2] Univ Granada, ETS Ingn Informat & Telecomunicac, C Periodista Daniel Saucedo Aranda S-N, E-18071 Granada, Spain

[3] Nanyang Technol Univ, Sch Comp Sci & Engn, 50 Nanyang Ave, Singapore 639798, Singapore

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2021年 / 114卷

关键词：

Social network analysis; Community detection; Natural language processing; Sentiment analysis; Social media mining; COMMUNITY DETECTION; SENTIMENT ANALYSIS;

D O I：

10.1016/j.future.2020.08.006

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The exponential growth of social media and online social networks (e.g., Facebook, Twitter, Instagram, and TikTok) has changed the daily lives of millions of people. The ease to accessing, gathering and processing available data and the high societal and industrial interest in such data have attracted the interest of a large of research disciplines. This special issue has been focused mainly on Data Science and Artificial Intelligence techniques, and their application to social network analysis. The issue provides a total of 12 selected papers (out of 65) that represent latest advances and developments in these areas. (C) 2020 Published by Elsevier B.V.

引用

页码：290 / 293

页数：4

共 24 条

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