Social Network Model Research Articles

History of the Mari Region and local Mari groups in the reflection of the latest Russian historiography: experience of social network modeling

The active growth of the use of information technologies has affected the methodology of historiographical research. This article uses the social network modeling technology for the analysis of the Russian historiography of the 2010s, devoted to various aspects of the Mari Territory and local Mari groups’ history. The information capabilities of the Scientific Electronic Library eLIBRARY.RU which contains the RSCI bibliographic database were used to achieve this aim. A selection of scientific papers on the studied subject matter was formed on the stated resource portal. It includes 627 articles from journals and conference proceedings authored by more than 270 experts in historical and related disciplines. From all the articles a pool of highly cited publications was identified, including 72 scientific papers, on the basis of which a network graph was created in the Gephi program, which allowed to visualize the connections between selected papers. The automated graph stacking helped to make 13 large clusters of publications as well as a number of "peripheral" publications. The article describes in detail the methods and technologies used in the research conduction, shows the general description of the identified "topography" of the stated subject area and its development trends, and describes the most popular research topics at the present stage. The study is novel in that it uses citation data to identify and analyze the structure of communication in this subject area. The conducted social network analysis of the scientific literature has shown that the considered subject area of modern Russian historiography demonstrates a pronounced progress in its development, as evidenced by a significant thematic expansion of research, a large and geographically wide of authors’ corpus, the involvement of a number of new historical sources, the use of new research approaches, as well as significant grant support for research.

It’s about time: revisiting reciprocity and triadicity in relational event analysis

Abstract Societies are intricate systems comprising interdependent social actors interconnected through diverse relationships. It has long been recognized that reciprocity and triadic closure are two fundamental components of this interdependence, and have as such been included in social network models. However, computational limitations and modelling complexity have meant that reciprodicity and triadic closure statistics included in such models have necessarily been simplified. Using novel computational and modelling approaches in relational event analysis, the aim of this paper is to explore a spectrum of endogenous network effect definitions, ranging from straightforward binary variables to complex, temporal functions accounting for the diminishing relevance of past events. Through simulation studies and real-world dataset analyses, it highlights the importance of comprehensively considering temporal dynamics and subtle assumptions in defining network effects. Neglecting these aspects can lead to significant pitfalls in the analysis. Fundamentally, the paper highlights the time-varying nature of reciprocity and triadic closure effects evident in empirical datasets. While exponential decay functions sometimes capture their temporal structure, more complex continuous functions of time often describe the intricate structure of both effects more precisely.

A Social Network Model for Analysis of Public Opinion Formation Process

A Social Network Model for Analysis of Public Opinion Formation Process

Influence of Entrepreneur Social Network on New Product Development Performance of Enterprises Under Intelligent Big Data

In the complex market environment, it is difficult for enterprises to innovate only by their own internal product resources. In order to improve the performance level of new product development, this paper puts forward the influence of entrepreneur social network on the performance of new product development under smart big data. The process of new product development is modeled, and the influence of entrepreneur social network on enterprise new product development performance is analyzed from two aspects: vertical network quality and horizontal network quality. According to the analysis results, build an intelligent big data platform, and use this platform to realize the mining and analysis of enterprise-related data; On this basis, EM algorithm is used to mine the performance data of enterprise new product development, DEA/AHP model is used to evaluate the performance level of enterprise new product development, and the evaluation results are combined with the influence model of entrepreneur social network on enterprise new product development performance to realize the influence analysis of entrepreneur social network on enterprise new product development performance. The experimental results show that the method is highly recognized by experts, which shows that the analysis results are reliable, and the correlation between entrepreneur social network and enterprise new product development performance can be obtained, which provides reference for enterprise new product development.

Social network, sustainable livelihood capital and risk response: an empirical analysis of rural China

ABSTRACT In rural China, farmers who have been “lifted out of poverty” still at risk of returning to poverty. The sustainable livelihood and risk coping ability of farmers are of great significance for rural development, and social networks have a distinctive effect on livelihood and risk coping ability of farmers. Based on full-coverage survey in a village, this paper adopts the entropy weight method, Social Network Analysis, and aims to build a social network model and reveal the impact of social networks on farmer’s livelihood and risk coping ability. The results show that the farmers’ livelihood capital based on social network has a significant amplification. Furthermore, social relationships can directly affect farmers’ ability to cope with livelihood risks. And, for farmers with higher social, information, human and financial capital, the effectiveness of social networks in enhancing their risk coping capacity is more significant. Overall, it is suggested that more attention should be paid to the positive role of social networks on the livelihood in the future. For rural development, social relationship networks should be continuously enriched and to strengthen its multiplier effect on farmers’ livelihood. And for farmer development, it should take measures to improve the mobilising capacity of social relations. And to amplify the effectiveness of various types of livelihood capital in risk response.

Keeping Siblings in Care Connected: Improving Relationship Stability via the Mockingbird Family Model

ABSTRACT Children and young people in Australian foster or kinship care are separated from their siblings for a range of reasons. These may include issues that are behavioural, sibling-related, the capacity of carers to host multiple children, or policies that enforce a one-child-per-bedroom rule. This study investigated strategies enhancing stability and meaningful connections among siblings within the Mockingbird Family, a social network model of foster and kinship caring. Case examples, network mapping, and visualisation of the Mockingbird Family networks are presented alongside results from thematic analysis of qualitative data. The findings suggest that the Mockingbird Family facilitates meaningful contact and sibling connections, even when coplacement was not possible. Sibling coplacement and contact provide crucial benefits such as emotional support, stability, shared experiences, and a sense of belonging, contributing to children’s and young people’s development, healing from trauma, and overall wellbeing. IMPLICATIONS Keeping sibling groups connected and in contact can reduce placement breakdown and contribute to their overall wellbeing. The Mockingbird Family model of foster care provides a range of options for keeping siblings meaningfully connected through coplacement within the same constellation and opportunities for other siblings to join in Mockingbird Family activities.

Rumor detection using BERT-based social circle and interaction network model

Rumor detection using BERT-based social circle and interaction network model

CGRec: a convolutional graph neural network-based recommendation model for social networks

CGRec: a convolutional graph neural network-based recommendation model for social networks

Towards modeling and analysis of longitudinal social networks

There are various methods for handling longitudinal data in graphs and social networks, all of which have an impact on the algorithms used in data analysis. This article provides an overview of limitations, potential solutions, and unanswered questions regarding different temporal data schemas in social networks that are comparable to existing techniques. Restricting algorithms to a specific time point or layer has no effect on the results. However, when applying these approaches to a network with multiple time points, adjusted algorithms or reinterpretation becomes necessary. Therefore, using a generic definition of temporal networks as one graph, we aim to explore how we could analyze longitudinal social networks with centrality measures. Additionally, we introduce two new measures, “importance” and “change”, to identify nodes with specific behaviors. We provide case studies featuring three different real-world networks exhibiting both limitations and benefits of the novel approach. Furthermore, we present techniques to estimate variations in importance and degree centrality over time.

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

Exploring the willingness and evolutionary process of public participation in community shared energy storage projects: Evidence from four first-tier cities in China

Community shared energy storage projects (CSES) are a key initiative for maintaining grid stability in the process of advancing the low-carbon transition of energy systems. Understanding the public's willingness to participate is fundamental to CSES implementation and promotion. However, limited research has focused on this topic. This study investigated the public's willingness to participate in CSES, based on an extended technology acceptance model, and used social network modeling to explore the evolutionary process of public participation in CSES under different policy combination scenarios. Empirical data were obtained from online questionnaire surveys in four first-tier cities in China (N = 1276). The results show that (1) the majority (approximately 71.48%) of residents have a higher willingness to participate in CSES, but their willingness to invest in it is lower (22.32 yuan/month). Residents with lower willingness mainly believed that they should not pay extra maintenance and management fees for energy storage equipment, followed by concerns about the safety of this equipment. (2) Economic benefits, functional value, risk affordability, peer influence, and personal altruism are the determining factors that affect public participation in CSES. (3) Social interaction is beneficial for information transmission, thereby reducing the cognitive differences among the public regarding CSES. (4) The “economic incentive and mandatory” policy combination has significantly increased the proportion of public participation in CSES, but there is psychological resistance from the public toward policy implementation in the early stages. The “economic incentive and publicity” policy combination can promote public participation in CSES slightly. The combination of economic incentives, mandatory policies, and publicity policies has a synergistic effect. These findings provide new insights for application research in energy technology innovation.

The Gendered Influence of Cross-Gender Friends on the Development of Adolescents’ Gender Role Attitudes

Gender role attitudes have important implications at the individual, family, and societal levels, underscoring the importance of understanding their development. Although parents predominate in the socialization of gender role attitudes in childhood, the influence of friends increases during adolescence. In particular, cross-gender friends can provide unique insights and perspectives on another gender group, making them likely to shape gender role attitudes. However, empirical explorations of the link between gender role attitudes and cross-gender friendships have been rare. Furthermore, the few studies conducted have yielded ambiguous results and have struggled to separate the influence of cross-gender friends from a selection of such friendships based on gender role attitudes. This study addresses these limitations by using longitudinal social network models to assess the co-evolution of friendship networks and gender role attitudes among 3,194 youth (Mage = 15.2, SD = 0.67, 51% female) in German schools. The findings reveal that, after accounting for selection processes, boys with cross-gender friends develop more egalitarian gender role attitudes, while girls’ attitudes are unaffected by cross-gender friendships. These findings suggest that promoting cross-gender friendships among adolescent boys holds the potential to foster egalitarian gender role attitudes.

Dynamic trust network-driven consensus modeling with endogenous adjustment and exogenous modification under a quasi-Z-number environment

The Z-number is widely used in decision-making because of its unique dual ability to capture human knowledge and facilitate judgments on realistic issues. To improve the applicability of Z-numbers, this paper puts forward the concept of quasi-Z-number and studies the social network and consensus models with quasi-Z-information to address multi-attribute group decision-making problems. First, a dynamic trust network among decision makers (DMs) is constructed. The endogenous adjustment mechanism is presented due to the interaction among DMs. Second, a feedback mechanism for non-consensus opinions is established. In the feedback, an exogenous modification mechanism is constructed through the minimal modification consensus model to help non-consensus DMs improve their consensus level. Furthermore, a minimum tolerance increment consensus model is proposed to cope with the situation where the consensus threshold is not reached within a specified number of iterations. Third, the practicability of the proposed decision-making system is illustrated by the case of a green energy investment in which all technicalities are shown. Its comprehensive performance is exhibited in the sensitivity analysis. The validity of the information form and methodology is demonstrated in the comparative analysis.

Structural Racism Conceptualization and Operationalization for Research for the U.S. HIV Epidemic: Findings from a Scoping Review and Implications for Advancing Research for Structural Interventions.

In the U.S., inequities by race/ethnicity in health outcomes, such as in the HIV epidemic, are long standing but have come to the forefront during the COVID-19 pandemic. There is growing recognition of the role of structural racism in racialized health inequities, yet the conceptualization and operationalization of structural racism in HIV research lags. We conducted a scoping review of existing published literature, between 1999-April 2024, conceptualizing and measuring structural racism's impact among people living with or at risk for HIV in the U.S. Our initial search yielded 236 unique articles, which after title and abstract screening yielded ten articles meeting full text review criteria. We then extracted key parameters, such as conceptualization, method of measurement of structural racism, study aims, design, and findings. Three of the articles were qualitative studies that conceptualized structural racism using (1) the social network model, (2) individual and structural intersectionality and (3) critical race theory. Operationalization of structural racism within the seven quantitative studies fell into three categories: (1) structural level, (2) a scale of experiences of racism, including structural racism, and (3) using explanatory demographic factors as downstream measures of the effects of structural racism. The variance in the conceptualization and operationalization of structural racism highlights the different interpretations of structural racism in its applications to the field of HIV research. Given the vast racial/ethnic inequities in HIV, we propose three overarching suggestions for next steps in improving the conduct of research on structural racism in HIV: (1) we must prioritize measuring racism past the individual and interpersonal levels to consider systemic factors at a societal level that manifest as structural racism to improve HIV outcomes in the U.S., (2) consider intergenerational effects of structural racism through the use of longitudinal data, and (3) broaden the agenda of structural racism to incorporate other systems of oppression. Additionally, broadening the scope of funding and inclusion of more researchers and individuals with lived experiences to support structural racism research to drive the scientific agenda and design of structural-level interventions will not only bolster achieving the U.S. Ending the HIV Epidemic goals but will do so by addressing inequities.

Introducing PJK’s Social Networking Model of Motives - From Image Creation to Public Actualisation: Expanding Maslow's Hierarchy in Digital Social Networks

Introducing PJK’s Social Networking Model of Motives - From Image Creation to Public Actualisation: Expanding Maslow's Hierarchy in Digital Social Networks

La influencia de las autocitaciones en las decisiones editoriales a partir de la simulación de rankings de revistas

The manipulation of the Journal Impact Factor (JIF) through editorial decisions that increase self-citations has been empirically observed in recent studies, sparking discussions on how to avoid such practices. This study aims to verify whether a policy of removing self-citations from the JIF calculation is enough to eliminate incentives for manipulation. We introduce an agent-based model of the academic social network formed by journals to test this hypothesis. We model journals as rational agents seeking to gain positions in the JIF ranking. The model was implemented in NetLogo and calibrated using data from the SCIMago Journal Ranking. Simulation results demonstrate that the model is capable of reproducing patterns observed in empirical networks and that the removal of self-citations can significantly reduce the adoption of manipulation strategies (e.g., reduction from 90% to 30%). This study contributes to understanding the role of self-citations in the JIF and provides tools for developing policies that promote scientific integrity.

Modelado de datos gubernamentales abiertos para mapeo de redes de escuelas similares en la Microrregión de Salvador - Bahía.

Introduction: The aim of this study is to represent similar schools located in the urban locality of the Microregion of Salvador - Bahia, to identify possible paths for collaboration among them, using the Technical Note "Profile of Similar Schools" 2014 from INEP as a basis. Methodology: Social network analysis and modeling from open governmental data were employed to investigate the interconnection between schools, considering their similar characteristics and educational indicators. Results: The open data includes information on school performance and socioeconomic levels, processed and analyzed through algorithms and the development of specific software. The study resulted in the web visualization of a multidimensional network, with several possibilities for analysis, showing that, out of the 408 schools, each one establishes connections with at least 70 other schools on average. Conclusion: This interconnection suggests the trend of schools to share similar features, offering potential to promote collaboration and sharing of resources and successful educational practices, contributing to better governance in education in the region.

Lifetime internal migration trajectories and social networks: Do repeat migrants fare worst?

While the economic benefits of internal migration are widely documented, the social costs of internal migration have received comparatively less attention. In addition, most studies focus on the impact of the last-recorded migration, ignoring the cumulative impact of successive migrations. Grounded in the life-course trajectory approach to migration and the convoy model of social networks, this paper addresses this gap by applying sequence and cluster analysis to retrospective data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 26 European countries to establish internal migration trajectories based on the timing, frequency, and direction of migration between NUTS-2 regions. The results reveal that differences in social networks between lifetime stayers, childhood migrants and one-time adult migrants are minimal. A more complex picture emerges for repeat migrants who account for half migrants and are split between return migrants, serial onward migrants, and circular migrants. Regression results show that repeat migrants – whether onward, return, or circular – display social networks less focused on family and more geographically dispersed, which results in a lower frequency of contact than lifetime stayers. However, repeat migrants report the same level of overall satisfaction with their social networks as lifetime stayers, which suggests that they start with different expectations than stayers or simply adjust their expectations in response to the social costs and benefits of migration.

Latent Variable Modeling of Social Networks with Directional Relations: An Exploration of Profile Similarity of Latent Factors

Statistical analysis of networks has gained increasing popularity in social and psychological sciences. This study introduces a latent variable model for self-reported directional relations within social networks, specifically focusing on predicting relationships based on actor attributes and latent traits. In a directional relation, the initiator is termed the “sender,” and the recipient is the “receiver.” Traditional symmetric similarity measures, such as Mahalanobis distance, often fall short of capturing the nuanced impact of sender and receiver latent traits in these scenarios. To overcome this limitation, we propose four distinct measures of trait similarity ( p sender , p receiver , m sender , and m receiver ), aiming to discern subtle distinctions between the impact of senders and receivers characteristics on the perceived social relations. We demonstrate the applicability and relevance of these measures in real-world network scenarios by applying our proposed framework to analyze a college friendship network. Among competing models, our findings affirm a potential non-linear trend between personality similarity and perceived friendship. Notably, the quadratic model with m receiver ( Q 8 ) outperformed others, exhibiting both significant linear and quadratic terms. Our results underscore the significance of the projected components of the receiver’s personality along with the sender’s orientation, with larger and smaller components both indicating a higher probability of perceived friendship.

Application of Deep Learning in Social Network Public Opinion Sentiment

With the rapid development of social network platform, in order to purify the network environment, prevent the abuse of public opinion information, and control public opinion in a very short time, this paper proposes the application research of deep learning in social network public opinion sentiment recognition and analysis. Through the construction of social network model and social network knowledge map and the analysis of key technology, a network public opinion algorithm based on deep learning is proposed, and a competitive public opinion information communication model in online social networks is constructed, and then a simulation experiment is conducted on the improved model of the constructed social networks. The result shows that in public opinion management and control, for users with larger node degree, this technology can effectively understand and obtain public opinion information in a very short time with faster and wider information propagation speed, so as to realize the effective control of public opinion information and the processing of massive information.