Social Network Patterns Research Articles

Association Between Source of Social Support and Depression Among Nursing Students During the COVID-19 Pandemic

Background: Nursing students have a higher risk of depression due to their already high academic burden, social isolation, pandemic loneliness, abrupt online learning, and financial difficulties during the COVID-19 pandemic lockdown. Although the disruption of social network patterns during the pandemic has been observed in previous studies, it is still not fully understood which source of social support is associated with depression among nursing students. Purpose: This study aimed to assess the correlations between sources of social support and depression among nursing students during the COVID-19 pandemic lockdown.Methods: An online cross-sectional study was conducted between May-September 2021. Nursing students (n=734) from seven universities across four provinces in Indonesia were recruited as participants using convenience sampling methods. Data were obtained through online questionnaires consisting of the Multidimensional Scale of Perceived Social Support (MSPSS) to assess social support, and the Patient Health Questionnaire-9 (PHQ-9) to assess depression. Spearman-Rank correlation tests were used to examine the correlations between sources of social support and depression. Results: Most of the nursing students (85.1%) were female with a mean age of 19.94 years (SD=1.42). Many nursing students (n=313; 42.6%) experienced depression. Most of the students (n=465; 63.4%) perceived high family support, moderate friends (n=415; 56.5%) and significant others' support (n=437; 59.5%). Of the three sources of social support (family, friends, and significant others), only family support had a significant inverse correlation with depression (Rho=-0.492, p<0.001).Conclusion: Family support had a significant inverse and moderate correlation with depression among nursing students during the COVID-19 pandemic. Our findings provided information to nursing educators to incorporate a strategy to maintain robust family support and regular depression assessments as part of the online learning curriculum. Therefore, it can be used to ameliorate depression among nursing students.

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.

Navigating the vulnerabilities and coping mechanisms of children in residential child care institutions: A qualitative exploration

Residential child care institutions around the globe play a significant role in protecting children in need of care and protection. While these facilities facilitate hope and rehabilitation, the displacement of children from their families introduces a variety of psychosocial stressors. This qualitative study aims to elucidate children’s vulnerabilities and coping mechanisms in residential child care institutions in Kerala, India. We conducted in-depth interviews with 21 children aged 8-16 living in institutional care within Ernakulam District, Kerala, India, registered under the Juvenile Justice (JJ) (Care and Protection of Children) Act 2015. Semi-structured interviews covered various domains, including demographics, emotional well-being, social interactions, behavioral issues, academics, and coping mechanisms. Attride-Stirling’s thematic network analysis was used in the data analysis to identify significant themes. The themes identified include (a) emotional well-being and self-perception, (b) behavioral problems, (c) social networking and interaction patterns, (d) educational and career aspirations, and (e) strengths and coping mechanisms. Our study presents the multifaceted challenges encountered by children in Kerala’s residential child care institutions, highlighting the need to create a nurturing environment that fosters child-centric practices that are essential to ensure their well-being and better prospects for their future.

Social Network Patterns of Creative Economy Actors in the Culinary Sector of Tourism Destination Areas in Padang City West Sumatra

This research was conducted to analyze the network patterns of creative economy actors in the culinary sector in Padang City. Data collection methods in this study began with documentation studies, in-depth interviews and non-participant observation. The approach used in this research is a qualitative approach with a descriptive analytical type and the withdrawal of informants through purposive sampling and snowball to see the relationship between creative economic actors in the culinary sector. The unit of analysis in this study is the individual. The results of this study indicate that creative economic actors in the culinary sector in Padang City have been able to build social networks. This can be seen in the distribution of culinary products that have been carried out by creative economic actors in the culinary sector outside the Padang City area. The social network pattern formed in the creative economic actors in the culinary sector is in a mixed social network pattern. In addition, creative economic actors build social network patterns in 4 forms, namely family-based social network patterns, friendships, joint activities and social media and customers. The social network pattern is spread in the distribution of culinary products that have been created and provides information on the acquisition of raw materials, strategies in product marketing and the results of product profits obtained by creative economic actors in the culinary sector. The novelty of this research is a mixed-based social network pattern.

Quality in care homes: How wearable devices and social network analysis might help.

Social network analysis can support quality improvement in care homes but traditional approaches to social network analysis are not always feasible in care homes. Recalling contacts and movements in a home is difficult for residents and staff and documentary and other sources of individual contacts can be unreliable. Bluetooth enabled wearable devices are a potential means of generating reliable, trustworthy, social network data in care home communities. In this paper, we explore the empirical, theoretical and real-world potential and difficulties in using Bluetooth enabled wearables with residents and staff in care homes for quality improvement. We demonstrate, for the first time, that a relatively simple system built around the Internet of Things, Bluetooth enabled wearables for residents and staff and passive location devices (the CONTACT intervention) can capture social networks and data in homes, enabling social network analysis, measures, statistics and visualisations. Unexpected variations in social network measures and patterns are surfaced, alongside "uncomfortable" information concerning staff time spent with residents. We show how technology might also help identify those most in need of social contact in a home. The possibilities of technology-enabled social network analysis must be balanced against the implementation-related challenges associated with introducing innovations in complex social systems such as care homes. Behavioural challenges notwithstanding, we argue that armed with social network information, care home staff could better tailor, plan and evaluate the effects of quality improvement with the sub-communities that make up a care home community.

Transnational patterns, social networks and self-help organizations for migrant women

Previous research shows the importance of building up self-help structures in a transnational perspective for the inclusion of migrant women who are fleeing their home countries because of war, violence, or different forms of vulnerability. The mobilization of self-help organizations through the intersection of transnationalism and gender is, in fact, a useful direction for a practice-oriented pedagogy directed both towards (1) the most vulnerable groups of women, or (2) those already empowered either as community leaders or network facilitators, other migrants and the whole native population. For this paper, we compare selected video-interviews of refugee women collected in Bulgaria and Italy, which are important receiving countries either at the South-Eastern or Southern external border of the European Union. The research questions of this comparative assessment include: 1) How do refugee women organize themselves for mutual help? 2) How do they build their social networks through transnational practices to bridge with the local people? 3) Is community or individual empowerment of refugee women better for implementation through ethnic lines or a gender perspective? The final aim of this investigation is to analyse various patterns of social networks’ creation among refugee women originating from different socio-cultural contexts. The research findings might be useful to instil inclusion practices which are apt to refugee women empowerment.

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies.

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

Social Networks in Improving the Learning Achievement of Zoning Pathway Students

Zoning policies create associations of students with different academic abilities and school backgrounds. The focus of this research is on zonal pathway students who have experienced improvements in terms of cognitive aspects and explains the problem of social networks in increasing the grades of zonal pathway students. This research uses a descriptive qualitative approach to examine the issues raised. The research subjects were determined using a purposive sampling technique, namely 12th-grade MIPA students. The research results explain that students have similar social network patterns among each other. Increasing student learning outcomes is dominated by peer group interaction within and outside the school environment. In this way, peers trigger or motivate students to improve learning outcomes.

Estruturas Sociais Cognitivas em Contexto de uma Sala de Ensino Médio

Abstract Cognitive social structure maps the social relationships perceived by actors. This study characterizes the social network pattern of a high school classroom and verifies the perceived ties and the accuracy of social relationships reported by 23 students. Analyses of the cognitive social structure aggregations verified the density of these relationships. Most participants show vulnerabilities and conglomerate analysis of network structural data shows different perceptions of density and modularity. Our results differ from the limited literature in the area regarding higher accuracy in subjects with a lower centrality degree and intermediation.

Analisa Jejaring Sosial dan Retorika Deliberatif

The aim of this research is to examine the phenomenon of deliberative rhetoric using the hashtag #SepakatDamai on Twitter to motivate the public. After the tragedy of riots between football supporters at the Kanjuruhan Stadium, it succeeded in triggering public attention and became a trending topic on Twitter. Many football supporters and the general public take an active role in voicing peace to reduce fighting. Through the hashtag #SepakatDamai, the public is persuaded to get involved in carrying out peace agreement actions so that, in the future, there will no longer be any hostility between football supporters. In the end, tweets from actors involved in peaceful demonstrations form social network patterns that can be utilized in deliberative rhetorical actions. The hashtag #SepakatDamai plays a role in mobilizing public opinion support for the peace agreement between football supporters. This discussion emerged spontaneously on Twitter and was widely discussed until it became a trending Twitter topic. This research uses the Social Network Analysis (SNA) method and then connects it with the Digital Movement of Opinion and Rhetoric Theory. This research stage began by determining the most dominant actor in the hashtag #SepakatDamai. After that, the researcher carried out an analysis of the posts made by that actor to determine the rhetorical phenomenon. The results of this research found that the hashtag #SepakatDamai succeeded in mobilizing public opinion about the football supporters' peace agreement. The most dominant actor is @mafiawasit, with a network structure value of degree 1.471, closeness 1.0, betweenness 0.000238, and eigenvector 1.0. The @mafiawasit account carries out deliberative rhetoric for the public by showing the facts that have occurred regarding the peace agreement between football supporters through posts on Twitter and retweeting similar posts so that the public follows the peaceful demonstration.

The social behaviour of the common bottlenose dolphin Tursiops truncatus (Montagu, 1821) in the coastal waters of Lampedusa Island (Strait of Sicily, Italy)

Abstract Research on social structure and residency provides important information on the dynamics of a population, improving knowledge of the species' behaviour and ecology. This study investigated the social network, site fidelity, and residency patterns of common bottlenose dolphins (Tursiops truncatus) off Lampedusa Island. Direct visual detection and photo‐identification methods were used to collect data during 124 boat‐based surveys conducted from 2013 to 2020. A total of 117 groups (mean size 4.5 ± 3.3) were sighted; 73 well‐marked animals were identified, of which 59% were resighted over the years. The estimate of social differentiation was 0.72 (SE = 0.07), suggesting differentiated social groups within the community. Data were somewhat representative of the true social system, as the correlation coefficient was close to 0.4. However, the half‐weight index averaged 0.07 (±0.03), indicating individuals' low tendency to associate. Five social clusters were identified with strong relationships between individuals within each cluster, in which 94% of maximum association indices were ≥0.4. The data also highlighted preferred and avoided associations and stable relationships. The removal of individuals with the highest values of strength and eigenvector centrality from the network did not affect its connectivity, suggesting inherent robustness. Despite the different levels of site fidelity among individuals, the results indicated rather high levels of yearly sighting rate. Temporary migration into and out of the area shown by this study indicates that Lampedusa coastal waters are an important, regularly used bottlenose dolphin habitat. These results suggest extensive ranging behaviour, which implies the need for networking between institutions and organizations from the various states bordering the Strait of Sicily to address conservation efforts and inform regional management of the Mediterranean bottlenose dolphin population.

A performance test of archaeological similarity-based network inference using New Guinean ethnographic data

Network analysis has become increasingly common within archaeological practice, yet little consensus exists as to what networks based on material culture actually reveal about ancient social life. One common approach to archaeological network inference relies on constructing similarity networks based on shared material types or stylistic categories between archaeological sites or contexts. Many studies implicitly or explicitly assume that the topology of similarity networks is a useful proxy for underlying patterns in ancient social networks, yet this basic assumption has not been rigorously evaluated. Here, I present a preliminary test of how well network measures inferred from material culture—in this case, bone daggers made on the island of New Guinea between 1845 and 2002—predict network measures derived from ethnographic accounts of social engagement between 1720 New Guinea communities. In this case study network distance partially predicts material similarity, and neighborhood/cluster identification algorithms partially identify similar patterning in underlying patterns of inter-community engagement. However, most commonly applied network measures of centrality are not strongly predicted by material cultural similarity. Similarity based network analysis is a powerful means of visualizing and exploring data, and can help in formulating archaeological hypotheses, but may be problematic as a direct inference procedure.

Social Exclusion and Conversion Factors

Scholarship on international students shows that despite university policies designed to create a welcoming atmosphere, international students still face social challenges. This paper applies the capabilities approach to reveal mechanisms that facilitate or constrain the social inclusion of married international graduate students. For married international graduate students, the personal factors (their level of study and marital status) bring with structural factors (e.g., visa policies, healthcare policies, cultural and linguistic barriers), which in combination lead to social exclusion. With one university case study, including original survey and interview data, we unpack these intertwined processes and find that married graduate students’ social relation and network patterns significantly differ from single graduate students and undergraduate students. They are less likely to attend campus events, interact with their colleagues, and interact with friends from other countries. As such, we challenge the conventional wisdom that access to higher education alone leads to social inclusion.

Emergent local structures in an ecosystem of social bots and humans on Twitter

Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.

Single-cell communication patterns and their intracellular information flow in synovial fibroblastic osteoarthritis and rheumatoid arthritis

BackgroundSynovial fibroblasts are critical for maintaining homeostasis in major autoimmune diseases involving joint inflammation, including osteoarthritis and rheumatoid arthritis. However, little is known about the interactions among different cell subtypes and the specific sets of signaling pathways and activities that they trigger. MethodsUsing social network analysis, pattern recognition, and manifold learning approaches, we identified patterns of single-cell communication in OA (osteoarthritis) and RA (rheumatoid arthritis). ResultsOur results suggest that OA and RA have distinct cellular communication patterns and signaling pathways. The LAMININ (Laminin) and COLLAGEN (Collagen) pathways predominate in osteoarthritis, while the EGF (Epidermal growth factor), NT (Neurotrophin) and CDH5 (Cadherin 5) pathways predominate in rheumatoid arthritis, with a central role for THY1 (Thy-1 cell surface antigen) +CDH11 (Cadherin 11) + cells. The OA opens the PDGF (Platelet-derived growth factors) pathway (driver of bone angiogenesis), the RA opens the EGF pathway (bone formation) and the SEMA3 (Semaphorin 3A) pathway (involved in immune regulation). Interestingly, we found that OA no longer has cell types involved in the MHC complex (Major histocompatibility complex) and their activity, whereas the MHC complex functions primarily in RA in the presentation of inflammatory antigens, and that the complement system in OA has the potential to displace the function of the MHC complex. The specific signaling patterns of THY1+CDH11+ cells and their secreted ligand receptors are more conducive to cell migration and lay the foundation for promoting osteoclastogenesis. This subpopulation may also be involved in the accumulation of lymphocytes, affecting the recruitment of immune cells. Members of the collagen family (COL1A1 (Collagen Type I Alpha 1 Chain), COL6A2 (Collagen Type VI Alpha 2 Chain) and COL6A1 (Collagen Type VI Alpha 1 Chain)) and transforming growth factor (TGFB3) maintain the extracellular matrix in osteoarthritis and mediate cell migration and adhesion in rheumatoid arthritis, including the PTN (Pleiotrophin) / THBS1 (Thrombospondin 1) interaction. ConclusionIncreased understanding of the interaction networks between synovial fibroblast subtypes, particularly the shared and unique cellular communication features between osteoarthritis and rheumatoid arthritis and their hub cells, should help inform the design of therapeutic agents for inflammatory joint disease.

Homophilic social networks and school adjustment among primary school pupils: Results and implications from a Zimbabwean study

Social network patterns were explored among Zimbabwean rural school pupils to determine their peer preferences and statuses. Participants were 173 school pupils attending grades 4–6 in a rural school in Zimbabwe (76 boys; 97 girls; age range 9 years to 12 years). The pupils completed a peer social statuses nomination measure, and a subsample of them participated in a brief interview to elaborate on their nominations. In addition, documents held by the school were analysed for context. Descriptive analysis of the data yielded four peer social statuses of popular (40%), accepted (30%), rejected (10%), and neglected (20%). Reasons for popularity included being good-mannered and possessing good socialising habits, as well as being outstanding in class. Boys who held positions of authority like class monitor and shared readily were popular. Girls’ social statuses were explained by good socialising habits and sharing available resources with others. Boys’ key popularity attributes were mathematical skills, as they would help the less able in class. Popular girls were also good at mathematics and socialising. The findings validate the view that social networking is significant to the prediction of social well-being among middle primary school pupils, and important for the children’s school engagement, or overall school adjustment.

The effect of “at home” network communication, off-site travel, and extracurricular activity on longitudinal social network development in study abroad

The nature of second language acquisition (SLA) in study abroad hinges on the type and quality of social relationships that sojourners develop. Social network analysis is now frequently used to describe this development, and to quantify its relationship with SLA outcomes. However, little is known about how different experiential components in study abroad affect sojourners' social network development and, consequently, language learning. The present study examines the effect of three aspects of the study abroad experience – travel outside of the study site; maintenance of “at home” social network communication; and participation in volunteer and workplace internship placements – on the longitudinal social network development of 28 U.S.-based sojourners studying abroad in Spain for one semester. Results identify four social network patterns, which for most students emerged in the first weeks of the semester and remained stable at the middle and end of the sojourn. This social network development was not greatly affected by communication with at-home networks, or by participation in internship placements; however, frequent travel outside the study site was shown to considerably hinder sojourners’ ability to develop social networks that were rich in target language use, or that reflected social “immersion” into the host community.

The Impact of Internet Use on the Well-Being of Rural Residents

With the full penetration of the Internet, the Internet has had a great impact on the production and life of rural residents. The article takes a rural residents’ group as its research object to explore the impact mechanism. Leveraging data from the Chinese Family Panel Studies (CFPS) in 2016, we employ a multivariate ordered logit model as an empirical approach to delve into the various dimensions of Internet usage. By examining different facets of Internet utilization, we aim to assess the effects of these distinct uses on the well-being of rural residents. Furthermore, we investigate the mediating role of social capital in understanding the collective well-being of this demographic. The findings of this study are as follows: (1) The utilization of the Internet yields a positive impact on the well-being of rural residents. (2) Social networks exhibit a significant positive influence on the well-being of rural residents; social solidarity demonstrates a significant negative impact on the well-being of rural residents. Additionally, social trust is found to have a significant negative effect on the well-being of rural residents. (3) Regarding mobile phone access, we identify a positive and significant effect on the well-being of rural residents when mediated by social networks and social trust. In contrast, social mutual aid does not exhibit a significant mediating effect. Among the patterns of mobile phone usage, social network and social mutual aid within the realm of social capital emerge as intermediate variables that affect the well-being of rural residents through Internet usage. However, it is worth noting that social trust does not have a significant effect in this regard. These results contribute to our understanding of how Internet usage and social capital interact to shape the well-being of rural communities.

Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance

Despite the success of machine learning models, the literature lacks their applications to identify the exploitation of non-public information. We address this gap by developing a tool, which ranks investors based on their suspiciousness. We achieve this by predicting the future trading decisions of retail investors based on their social connections in an insider network. Particularly, the high predictability of the investor’s trading behavior with data on her/his social neighborhood can indicate that she/he takes advantage of her/his social connections and trades on non-public information. Our system captures complex and cyclical patterns in investor social networks with Graph Networks trained with full investor-level transaction data. We show that using data on social neighborhoods significantly improves the model’s performance in predicting investors’ trading behavior. The results are robust regarding the different groups of investors, trading windows, trading directions, and outliers. We demonstrate the tool by ranking suspicious investors and companies, with 12 out of 153 companies having a statistically significant concentration of directors (p-value < 0.05) with suspicious trading behavior. The system provides regulators with a valuable tool for prioritizing market surveillance efforts.

On quantification and maximization of information transfer in network dynamical systems

Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.