多层节点对齐网络的协同进化,其各层代表不同的社会关系。

Q1 Mathematics
Computational Social Networks Pub Date : 2017-01-01 Epub Date: 2017-11-06 DOI:10.1186/s40649-017-0047-1
Ashwin Bahulkar, Boleslaw K Szymanski, Kevin Chan, Omar Lizardo
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引用次数: 0

摘要

研究背景我们研究了大学生三层节点对齐网络的共同演化。第一层是根据前四个学期的重复调查中收集到的感知显著性提名定义的;第二层是根据手机通话和短信记录代表学生实际互动的行为层;第三层是蓝牙连接所暗示的潜在面对面互动的行为层:我们解决了四个相互关联的问题。首先,我们询问其中一层链接的形成或解体是先于还是后于另一层相应链接的形成或解体(时间依赖性)。其次,我们探究各层之间时间依赖关系的原因。对于那些得到证实的时间依赖关系,我们会衡量这种依赖关系的预测能力。第三,我们观察提名的进展情况以及导致提名的各个阶段。最后,我们研究了对称(非定向)和非对称(定向)链接的解散率差异是否在所有层中同时存在:结果:我们发现各层之间存在很强的互时依赖模式。特别是,行为层中任何一个边缘的形成通常都先于提名层中相应边缘的形成。相反,提名层中某一联系的衰减通常先于交流和搭配强度的下降。最后,与对称边缘连接的节点相比,非对称提名边缘连接的节点的总体交流和搭配量更低,非对称交流流量更大:我们发现,认知突出联系人的创建和解散与交流和搭配行为具有时间依赖性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Coevolution of a multilayer node-aligned network whose layers represent different social relations.

Coevolution of a multilayer node-aligned network whose layers represent different social relations.

Coevolution of a multilayer node-aligned network whose layers represent different social relations.

Coevolution of a multilayer node-aligned network whose layers represent different social relations.

Background: We examine the coevolution of three-layer node-aligned network of university students. The first layer is defined by nominations based on perceived prominence collected from repeated surveys during the first four semesters; the second is a behavioral layer representing actual students' interactions based on records of mobile calls and text messages; while the third is a behavioral layer representing potential face-to-face interactions suggested by bluetooth collocations.

Methods: We address four interrelated questions. First, we ask whether the formation or dissolution of a link in one of the layers precedes or succeeds the formation or dissolution of the corresponding link in another layer (temporal dependencies). Second, we explore the causes of observed temporal dependencies between the layers. For those temporal dependencies that are confirmed, we measure the predictive capability of such dependencies. Third, we observe the progress towards nominations and the stages that lead to them. Finally, we examine whether the differences in dissolution rates of symmetric (undirected) versus asymmetric (directed) links co-exist in all layers.

Results: We find strong patterns of reciprocal temporal dependencies between the layers. In particular, the creation of an edge in either behavioral layer generally precedes the formation of a corresponding edge in the nomination layer. Conversely, the decay of a link in the nomination layer generally precedes a decline in the intensity of communication and collocation. Finally, nodes connected by asymmetric nomination edges have lower overall communication and collocation volumes and more asymmetric communication flows than the nodes linked by symmetric edges.

Conclusion: We find that creation and dissolution of cognitively salient contacts have temporal dependencies with communication and collocation behavior.

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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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