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Computational Models for Social Network Analysis: A Brief Survey

Author:

Jie TangAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 921 - 925

https://doi.org/10.1145/3041021.3051101

Published: 03 April 2017 Publication History

Abstract

With the exponential growth of online social network services such as Facebook and Twitter, social networks and social medias become more and more important, directly influencing politics, economics, and our daily life. Mining big social networks aims to collect and analyze web-scale social data to reveal patterns of individual and group behaviors. It is an inherently interdisciplinary academic field which emerged from sociology, psychology, statistics, and graph theory. In this article, I briefly survey recent progress on social network mining with an emphasis on understanding the interactions among users in the large dynamic social networks. I will start with some basic knowledge for social network analysis, including methodologies and tools for macro-level, meso-level and microlevel social network analysis. Then I will give an overall roadmap of social network mining. After that, I will describe methodologies for modeling user behavior including state-of-the-art methods for learning user profiles, and introduce recent progress on modeling dynamics of user behaviors using deep learning. Then I will present models and algorithms for quantitative analysis on social interac- tions including homophily and social influence.Finally, I will introduce network structure model including social group formation, and network topology generation. We will introduce recent developed network embedding algorithms for modeling social networks with the embedding techniques. Finally, I will use several concrete examples from Alibaba, the largest online shopping website in the world, and WeChat, the largest social messaging service in China, to explain how online social networks influence our offline world.

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Index Terms

Computational Models for Social Network Analysis: A Brief Survey
1. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Social media networks

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Published In

cover image ACM Other conferences

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://doi.org/10.1007/978-3-662-68100-8_2
Jiang RLiu C(2022)Enhancing Marine Data Transmission with Socially Aware Resilient Vessel NetworksIEEE Wireless Communications10.1109/MWC.004.210061229:3(72-78)Online publication date: Jun-2022
https://doi.org/10.1109/MWC.004.2100612
Gong JDu WLi HLi QZhao YYang KWang Y(2021)Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud SystemsIEEE Access10.1109/ACCESS.2020.30351629(47753-47766)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3035162
Liu W(2021) Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking Internet Technology Letters10.1002/itl2.2884:5Online publication date: 22-Mar-2021
https://doi.org/10.1002/itl2.288
Pan XHu RLi D(2020)Social-IFD: Personalized Influential Friends Discovery Based on Semantics in LBSNICC 2020 - 2020 IEEE International Conference on Communications (ICC)10.1109/ICC40277.2020.9148703(1-6)Online publication date: Jun-2020
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Kumar Behera RKumar Rath SMisra SDamaševičius RMaskeliūnas R(2019)Distributed Centrality Analysis of Social Network Data Using MapReduceAlgorithms10.3390/a1208016112:8(161)Online publication date: 9-Aug-2019
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