Social networks from dailies: the observer’s point of view

In this paper, we explore the hypothesis that the social network described by an observer in a written document provides a discernible cross-section of the underlying social network. Specifically, we propose that the subnetwork described by the observer is extracted from large social network with a Barabási-Albert structure, following two key premises: (1) the observer identifies and reports nodes and interactions in his local environment, and (2) the observer records the most relevant nodes—those with a high number of connections—as well as the most significant portions of their local environment. We test this hypothesis using personal dailies written in New Spain and Mexico between 1776 and 1873. We compare the structure of these dailies networks to one depicted by a typical subnetwork of a Barabási-Albert network, obtained following the two premises mentioned above. To support our findings, we compare them to the social networks described in two well-known novels. In this work, we implicitly assume the hypothesis that there is a complete, non-subjective social network, including our observer, with a Barabási-Albert structure. The evolution of this underlying social network can shed light on the dynamics of a society over time. Our approach could give place to indicators of the changes in the structure of this enveloping network. This is especially important since, although our historical sources do not offer sufficient data to reconstruct the processes in their entirety, the approach could supply clues or fragments from which we could understand the structure and dynamics of societies over time.

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Publication Details

Journal
Applied Network Science
Published
2024-11-08
DOI
https://doi.org/10.1007/s41109-024-00681-9
Citations
3
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.84
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article

Social networks from dailies: the observer’s point of view

Edgardo Galán-Vásquez, Edgardo Ugalde, José Antonio Motilla-Chávez
3 citations
Applied Network Science
Complex Network Analysis Techniques
0.84
article

Social networks from dailies: the observer’s point of view

Edgardo Galán-Vásquez, Edgardo Ugalde, José Antonio Motilla-Chávez
article en
3 citations

Abstract

In this paper, we explore the hypothesis that the social network described by an observer in a written document provides a discernible cross-section of the underlying social network. Specifically, we propose that the subnetwork described by the observer is extracted from large social network with a Barabási-Albert structure, following two key premises: (1) the observer identifies and reports nodes and interactions in his local environment, and (2) the observer records the most relevant nodes—those with a high number of connections—as well as the most significant portions of their local environment. We test this hypothesis using personal dailies written in New Spain and Mexico between 1776 and 1873. We compare the structure of these dailies networks to one depicted by a typical subnetwork of a Barabási-Albert network, obtained following the two premises mentioned above. To support our findings, we compare them to the social networks described in two well-known novels. In this work, we implicitly assume the hypothesis that there is a complete, non-subjective social network, including our observer, with a Barabási-Albert structure. The evolution of this underlying social network can shed light on the dynamics of a society over time. Our approach could give place to indicators of the changes in the structure of this enveloping network. This is especially important since, although our historical sources do not offer sufficient data to reconstruct the processes in their entirety, the approach could supply clues or fragments from which we could understand the structure and dynamics of societies over time.

Applied Network ScienceVol. 9(1)
Autonomous University of San Luis Potosí (MX), Universidad Nacional Autónoma de México (MX)
Reduced inequalities
Openalex Percentile: Top 27%
Complex Network Analysis Techniques
0.84
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