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Review of Structural Diversity Studies on Social Networks |
Lu Yingjie1,2,Zhang Yinglong2() |
1School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363099, China 2School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363099, China |
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Abstract [Objective] This paper reviews the latest developments of the structural diversity studies on social networks and discusses their future directions. [Coverage] We searched the Web of Science, Microsoft Academic, DBLP, CNKI, Wanfang Data and VIP with “Structural Diversity”, “Structural Diversity and Social Networks ”. A total of 55 representative and related literature published from April 2012 to April 2022 were retrieved. [Methods] First, we traced to the source of structural diversity studies and analyzed their existing issues. Then, we examined the structural diversity research from three perspectives: model improvements, efficient algorithms, and practical applications. Finally, we discussed future works. [Results] Structural diversity is a model based on network topology features, which studies factors affecting individuals’ major decision makings. The original model has the bad universality and low precision issues. Combined with graph mining technology, the new model performs well and has been applied in many fields. [Limitations] We only summarized research on structural diversity and did not compare them with other social contagion theories. [Conclusions] Graph mining algorithm could improve the structural diversity model in group division. Structural diversity is an indicator for finding highly influential nodes and required by efficient search algorithms. Structural diversity has been applied in the fields of behavior and link predictions. Features optimizing this model merit more research to evaluated their performance.
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Received: 30 November 2021
Published: 23 September 2022
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Fund:National Natural Science Foundation of China(61762036) |
Corresponding Authors:
Zhang Yinglong,ORCID:0000-0002-3293-3257
E-mail: zhang_yinglong@126.com
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