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Modeling and analyzing users’ behavioral strategies with co-evolutionary process
Computational Social Networks Pub Date : 2021-03-10 , DOI: 10.1186/s40649-021-00092-1
Yutaro Miura , Fujio Toriumi , Toshiharu Sugawara

Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.

中文翻译:

通过协同进化过程对用户的行为策略进行建模和分析

社交网络服务(SNS)经常被许多人以其社交关系和目的以各种动机和意图来使用,因此,导致在SNS上发布/使用内容的策略多种多样。因此,重要的是要了解各个策略的不同之处,具体取决于它们的网络位置和周围环境。为此,通过使用称为代理人的用户博弈模型并提出一种称为多世界遗传算法的协同进化算法来为每个用户发展不同的策略,我们研究了各个策略的差异,并在人工网络中比较了结果以及Facebook自我网络的内容。根据我们的实验,我们发现代理商没有选择搭便车策略,这意味着尽管在Facebook网络中阅读了其他用户发布的文章和评论,但这种策略通常具有成本效益,并且通常出现在人工网络中。我们还发现,主要评论发布的文章/评论并且很少发布自己的文章的代理出现在Facebook网络中,但是没有出现在连接的最近邻居网络中,尽管我们认为这种用户实际上存在于真实的世界SNS。我们的实验模拟还表明,通过分析对各种自我网络的评论奖励的差异所产生的影响,朋友的数量是确定用户对SNS采取策略的关键因素。尽管此策略通常具有成本效益,并且通常出现在人工网络中。我们还发现,主要评论发布的文章/评论并且很少发布自己的文章的代理出现在Facebook网络中,但是没有出现在连接的最近邻居网络中,尽管我们认为这种用户实际上存在于真实的世界SNS。我们的实验模拟还表明,通过分析对各种自我网络的评论奖励的差异所产生的影响,朋友的数量是确定用户对SNS采取策略的关键因素。尽管此策略通常具有成本效益,并且通常出现在人工网络中。我们还发现,主要评论发布的文章/评论并且很少发布自己的文章的代理出现在Facebook网络中,但是没有出现在连接的最近邻居网络中,尽管我们认为这种用户实际上存在于真实的世界SNS。我们的实验模拟还表明,通过分析对各种自我网络的评论奖励的差异所产生的影响,朋友的数量是确定用户对SNS采取策略的关键因素。尽管我们认为这种用户实际上存在于现实世界的SNS中。我们的实验模拟还表明,通过分析对各种自我网络的评论奖励的差异所产生的影响,朋友的数量是确定用户对SNS采取策略的关键因素。尽管我们认为这种用户实际上存在于现实世界的SNS中。我们的实验模拟还表明,通过分析对各种自我网络的评论奖励的差异所产生的影响,朋友的数量是确定用户对SNS采取策略的关键因素。
更新日期:2021-03-10
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