Coevolution of a multilayer node-aligned network whose layers represent different social relations
Abstract 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 stu...
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doaj-a7f532768cf948838277baad7c6c22da2021-03-02T10:41:47ZengSpringerOpenComputational Social Networks2197-43142017-11-014112210.1186/s40649-017-0047-1Coevolution of a multilayer node-aligned network whose layers represent different social relationsAshwin Bahulkar0Boleslaw K. Szymanski1Kevin Chan2Omar Lizardo3Rensselaer Polytechnic InstituteRensselaer Polytechnic InstituteUS Army Research LaboratoryUniversity of Notre DameAbstract 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.http://link.springer.com/article/10.1186/s40649-017-0047-1Coevolution of network layersMachine learning for social network analysisNetSense dataset |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ashwin Bahulkar Boleslaw K. Szymanski Kevin Chan Omar Lizardo |
spellingShingle |
Ashwin Bahulkar Boleslaw K. Szymanski Kevin Chan Omar Lizardo Coevolution of a multilayer node-aligned network whose layers represent different social relations Computational Social Networks Coevolution of network layers Machine learning for social network analysis NetSense dataset |
author_facet |
Ashwin Bahulkar Boleslaw K. Szymanski Kevin Chan Omar Lizardo |
author_sort |
Ashwin Bahulkar |
title |
Coevolution of a multilayer node-aligned network whose layers represent different social relations |
title_short |
Coevolution of a multilayer node-aligned network whose layers represent different social relations |
title_full |
Coevolution of a multilayer node-aligned network whose layers represent different social relations |
title_fullStr |
Coevolution of a multilayer node-aligned network whose layers represent different social relations |
title_full_unstemmed |
Coevolution of a multilayer node-aligned network whose layers represent different social relations |
title_sort |
coevolution of a multilayer node-aligned network whose layers represent different social relations |
publisher |
SpringerOpen |
series |
Computational Social Networks |
issn |
2197-4314 |
publishDate |
2017-11-01 |
description |
Abstract 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. |
topic |
Coevolution of network layers Machine learning for social network analysis NetSense dataset |
url |
http://link.springer.com/article/10.1186/s40649-017-0047-1 |
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