直接互惠和网络结构对社交网络服务持续繁荣的影响。

Q1 Mathematics
Computational Social Networks Pub Date : 2017-01-01 Epub Date: 2017-05-26 DOI:10.1186/s40649-017-0038-2
Kengo Osaka, Fujio Toriumi, Toshihauru Sugawara
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引用次数: 15

摘要

背景:社交网络服务(Social networking services, sns)作为一种交流工具被广泛应用于各种目的。社交网站依赖于用户的个人活动,这需要付出一定的成本和努力,因此用户自愿继续参与社交网站的原因尚不清楚。由于社交网络的结构与公共物品(PG)游戏的结构相似,一些研究关注的是为什么通过修改PG游戏,自愿活动成为一种最优策略。然而,他们的模型并不包括用户之间的直接互惠,尽管互惠是人类社会发展和维持合作的关键机制。提出的方法:我们开发了一个抽象的SNS模型,称为互惠奖励和元奖励游戏,通过扩展现有模型包括直接互惠。然后,我们研究了社交网络中的直接互惠如何通过发表文章和评论促进与参与社交网络相对应的合作,以及用户网络结构如何影响用户使用互惠奖励游戏的策略。实验结果:我们在各种复杂网络和Facebook实例网络上运行互惠奖励游戏,发现出现了两种类型的稳定合作。首先,互惠在完全图中略微提高了合作率,但由于合作的不稳定性,这种提高是不显著的。然而,这种不稳定性可以通过以下两个假设来避免:高度有趣,即文章被高概率阅读,以及对互惠和非互惠代理的不同态度。然后,我们提出了半搭便车的概念来解释什么策略维持合作主导的情况。其次,在不做任何额外假设的情况下,我们指出一定的WS网络结构会影响用户的最优策略,有利于稳定的合作。详细分析了两种合作优势情境的不同特征,以及互惠主体的记忆对合作的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Effect of direct reciprocity and network structure on continuing prosperity of social networking services.

Effect of direct reciprocity and network structure on continuing prosperity of social networking services.

Effect of direct reciprocity and network structure on continuing prosperity of social networking services.

Effect of direct reciprocity and network structure on continuing prosperity of social networking services.

Background: Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users' individual activities associated with some cost and effort, and thus it is not known why users voluntarily continue to participate in SNSs. Because the structures of SNSs are similar to that of the public goods (PG) game, some studies have focused on why voluntary activities emerge as an optimal strategy by modifying the PG game. However, their models do not include direct reciprocity between users, even though reciprocity is a key mechanism that evolves and sustains cooperation in human society.

Proposed methods: We developed an abstract SNS model called the reciprocity rewards and meta-rewards games that include direct reciprocity by extending the existing models. Then, we investigated how direct reciprocity in an SNS facilitates cooperation that corresponds to participation in SNS by posting articles and comments and how the structure of the networks of users exerts an influence on the strategies of users using the reciprocity rewards game.

Experimental results: We run reciprocity rewards games on various complex networks and an instance network of Facebook and found that two types of stable cooperation emerged. First, reciprocity slightly improves the rate of cooperation in complete graphs but the improvement is insignificant because of the instability of cooperation. However, this instability can be avoided by making two assumptions: high degree of fun, i.e. articles are read with high probability, and different attitudes to reciprocal and non-reciprocal agents. We then propose the concept of half free riders to explain what strategy sustains cooperation-dominant situations. Second, we indicate that a certain WS network structure affects users' optimal strategy and facilitates stable cooperation without any extra assumptions. We give a detailed analysis of the different characteristics of the two types of cooperation-dominant situations and the effect of the memory of reciprocal agents on cooperation.

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来源期刊
Computational Social Networks
Computational Social Networks Mathematics-Modeling and Simulation
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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