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Social Structures Characterized by Correlations between the Social Network.

Jesse Marin

Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Japan

Published Date: 2022-04-14

Jesse Marin*

Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Japan

*Corresponding Author:
Jesse Marin
Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Japan
E-mail:  [email protected]

Received date: March 14, 2022, Manuscript No. IPEJBIO-22-12997; Editor assigned date: March 17, 2022, PreQC No. IPEJBIO-22-12997 (PQ); Reviewed date: March 28, 2022, QC No. IPEJBIO-22-12997; Revised date: April 7, 2022, Manuscript No. IPEJBIO-22-12997 (R); Published date: April 14, 2022, DOI: 10.36648/1860-3122.18.4.020
Citation: Marin J (2022) Social Structures Characterized by Correlations between the Social Network. Electronic J Biol, 18(4): 1-2.

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Socioeconomic imbalances, which universally characterize all modern societies, are partially induced by the uneven distribution of economic power between individuals. Such disparities are among the key forces behind the emergence of social inequalities, which in turn leads to social stratification and spatial segregation in social structures characterized by correlations between the social network, living environment and socioeconomic status of people. Although this hypothesis was drawn a long time ago, the empirical observation of spatial, socioeconomic and structural correlations in large social systems has been difficult as it requires simultaneous access to multimodal characters for a large number of individuals. Our aim in this study is to find evidence of social stratification through the analysis of a combined large-scale anonymized dataset that discloses simultaneously the social interactions, frequent locations and the economic status of millions of individuals.

The identification of socioeconomic classes is among the historical questions in the social sciences with several competing hypothesis proposed on their structure and dynamics. One broadly accepted definition identifies lower, middle and upper classes based on the socioeconomic status of individuals. These classes can be further used to indicate correlations characterizing the social system. People who live in the same neighbourhood may belong to the same class, and may have similar levels of education, jobs, income, ethnic background, and may even share common political views.

The economic capacity of individuals arguably correlates with their professional occupation, education level and housing, which in turn determine their social status and environment. At the same time, status homophily, i.e. people's tendency to associate with others of similar social status, has been argued to be an important mechanism that drives the creation of social ties. Our hypothesis is that these two effects, diverse socioeconomic status and status homophily, potentially lead to the emergence of a stratified struc-

ture in the social network where people of the same social class tend to be better connected among themselves than with people from other classes. A similar hypothesis had been suggested earlier but its empirical verification had been impossible until now as this would require detailed knowledge about the social structure and precise estimators of individual economic status. In the following, our main contribution is to clearly identify signatures of social stratification in a representative society-level dataset, which contains information on both the social network structure and the economic status of people.

Signatures of Social Stratification

In order to investigate signatures of social stratification, we combine the bank transaction data with data disclosing the social connections between the bank's customers. To identify social ties, we use a mobile communication dataset, provided by one mobile phone operator in the country, with a customer set that partially overlaps with the user set found in the bank data (for details on data matching policy, see Data and material). To best estimate the social network, we connect people who communicated with each other at least once via calling or SMS during the observation period of 21 months between January 2014 and September 2015, but we remove non-human actors, such as call centres and commercial communicators by using a recursive filtering method.

Knowledge of local environments, such as effective agricultural or animal husbandry techniques, was vital to the survival of these early migrants. Evolutionary epistemology views the gaining of knowledge as an adaptive process with blind variation and selective retention. Communication of knowledge between individuals is also an efficient means to spread this discovered locally adapted knowledge. Similarly, models of social learning theory stress the importance of social learning in the spread of innovations. Here we model the adaptation of a population to the local environment using an evolutionary model with natural selection, mutation and communication. The knowledge of an individual determines his or her fitness. Evolutionary psychology and archaeology posit that the human mind is modular and that this modularity is shaped by evolution and facilitates understanding of local environments. Conjugate to this modularity must be dynamical exchange of corpora of knowledge between individuals.

These similarities together with homophily, i.e. the tendency of people to build social ties with similar others, strongly influence the structure of social interactions and also have indisputable consequences on the global social network. The coexistence of social classes and homophily may lead to a strongly stratified social structure where people of the same social class tend to be better connected among each other, while connections between different classes are less frequent than one would expect from structural characteristics only. These correlations may further determine the living environment and mobility of people leading to spatial segregation and specific commuting patterns characterizing people from similar social classes.

Analysis of the social Structure

The observation of such correlations should be possible through the analysis of the social structure. Research on social networks has recently been accelerated through the advent of new technologies which allow the collection of detailed digital footprints of interactions of large numbers of people. These advancements have allowed us to observe that social networks appear with heterogeneous connection patterns, are structurally, spatially and temporally correlated, and to identify various social mechanisms driving their evolution. However, although such datas-ets may contain some information about individual characteristics, they commonly miss one important dimension: They do not provide any direct estimator of the economic status of people, which could strongly influence their connection preferences and may determine the social position of an individual in the global social network. Coarse-grained information details about people's economic status are typically provided as statistical census measures without disclosing the underlying social structure, or by social surveys covering a small and less representative population.

In this paper, we aim to close this gap through the analysis of a combined dataset collecting the social interactions, proxy location and economic situation of a large set of individuals. More precisely, we analyse the transaction and purchase history coupled with time-resolved, spatially detailed mobile phone interactions of millions of anonymized inhabitants of a Latin American country over eight months (for a detailed data description, see Data and material). After introducing precise indicators of economic status, we show that not only individual income but also debt is distributed unevenly in accordance with the Pareto principle. Through the detection of homophilic correlations in the social structure, we provide strong empirical evidence of the stratified intra- and inter-class structure of the social network, and the existence of assortative socioeconomic correlations and ‘rich clubs’. Finally, we present quantitative results about the relative spatial distribution and typical commuting distances of people from different socioeconomic classes.

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