Properties Of Social Networks Research Articles

Cluster-based network proximities for arbitrary nodal subsets

The concept of a cluster or community in a network context has been of considerable interest in a variety of settings in recent years. In this paper, employing random walks and geodesic distance, we introduce a unified measure of cluster-based proximity between nodes, relative to a given subset of interest. The inherent simplicity and informativeness of the approach could make it of value to researchers in a variety of scientific fields. Applicability is demonstrated via application to clustering for a number of existent data sets (including multipartite networks). We view community detection (i.e. when the full set of network nodes is considered) as simply the limiting instance of clustering (for arbitrary subsets). This perspective should add to the dialogue on what constitutes a cluster or community within a network. In regards to health-relevant attributes in social networks, identification of clusters of individuals with similar attributes can support targeting of collective interventions. The method performs well in comparisons with other approaches, based on comparative measures such as NMI and ARI.

From a Bird's Eye View: Whole Social Networks in Adult Day Care Centers and Continuing Care Retirement Communities.

Background and ObjectivesThe present study describes whole social networks in 4 adult day care centers (ADCCs) and 4 continuing care retirement communities (CCRCs) in Israel.MethodEach respondent received a list of names of all individuals receiving services in the respective ADCC or CCRC and was asked to indicate whom he/she knows from the list. We derived whole social network properties and used hierarchical cluster analysis to group network settings. We further examined the ability of the social network data to classify respondents as members of either an ADCC or a CCRC.ResultsMany social network properties were more favorable in CCRCs than in ADCCs. A striking finding of the present study is that one can classify with a relatively high degree of accuracy a respondent as belonging to an ADCC or a CCRC, simply based on his or her social properties (specifically, number of people who know the participant and are known by the participant).ImplicationsDespite some similarities between CCRCs and ADCCs, CCRCs likely allow for more inclusive and active social relations. This information should be valuable to administrators and care providers.

Analysis of social interaction network properties and growth on Twitter

The complex topology of real networks allows its actors to change their functional behavior. Network models provide better understanding of the evolutionary mechanisms being accountable for the growth of such networks by capturing the dynamics in the ways network agents interact and change their behavior. Considerable amount of research efforts is required for developing novel network modeling techniques to understand the structural properties of such networks, reproducing similar properties based on empirical evidence, and designing such networks efficiently. In this study, we first demonstrate how to construct social interaction networks using social media data and then present the key findings obtained from the network analytics. We analyze the characteristics and growth of online social interaction networks, examine the network properties and derive important insights based on the theories of network science literature. We observed that the degree distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interaction. While the network elements and average user degree grow linearly each day, densities of such networks tend to become zero. Largest connected components exhibit higher connectivity (density) when compared with the whole graph. Network radius and diameter become stable over time evidencing the small-world property. We also observe increased transitivity and higher stability of the power-law exponents as the networks grow. Since the data is specific to the Purdue University community, we also observe two very big events, namely Purdue Day of Giving and Senator Bernie Sanders’ visit to Purdue University as part of Indiana Primary Election 2016. The social interaction network properties that are revealed in this study can be useful in disseminating targeted information during planned special events.

Social network

PurposeThe purpose of this paper is to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda.Design/methodology/approachThe study employed a cross-sectional research design and data were collected from a total of 400 poor households located in Northern, Eastern, Central and Western Uganda. The authors adopted ordinary least square hierarchical regression analysis to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. The effects were determined by calculating the significant change in coefficient of determination (R2) between the dimensions of social network in explaining financial inclusion. In addition, analysis of variance was also used to test for variation in perceptions of the poor about being financially included.FindingsThe findings revealed that the dimensions of ties and interaction significantly explain financial inclusion of the poor in rural Uganda. Contrary to previous studies, the results indicated that interdependence as a dimension of social network is not a significant predictor of financial inclusion of the poor in rural Uganda. Combined together, the dimensions of social network explains about 16.6 percent of the variation in financial inclusion of the poor in rural Uganda.Research limitations/implicationsThe study was purely cross-sectional, thus, ignoring longitudinal survey design, which could have investigated certain characteristics of the variable over time. Additionally, although a total sample amounting to 400 poor households was used in the study, the results cannot be generalized since other equally marginalized groups such as the disabled persons, refugees, and immigrants were not included in this study. Furthermore, the study used only the questionnaire to elicit responses from the respondents. The use of interview was ignored during data collection.Practical implicationsPolicy makers, managers of financial institutions, and financial inclusion advocates should consider social network dimensions of ties and interaction as conduits for information flow and sharing among the poor including the women and youth about scarce financial resources like loans. Advocacy towards creation of societal network that brings the poor together in strong and weak ties is very important in scaling up access to and use of scarce financial services for improving economic and social well-being.Originality/valueContrary to previous studies, this particular study test the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Thus, it methodologically isolates the individual contribution of each of the dimensions of social network in explaining financial inclusion of the poor. The authors found that only ties and interaction are significant predictors of financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that not all dimensions of social network are significant predictors of financial inclusion as opposed to previous empirical findings.

The Drivers of Collaborative Success Between Rural Economic Development Organizations

The nonprofit literature has directed attention to exploring how features of the broader structure of exchanges within regional collaboration networks impact the dynamics and outcomes of a single partnership. This study examines how partners’ relative positions within a collaboration network impact their interdependence and collaborative success. Our analysis of 298 collaborations between 98 economic development organizations operating in an economically distressed rural region demonstrates that social network properties—structural embeddedness and relative centrality—have substantial effects on exchange partners’ collaborative success. We also investigate whether network effects are mediated by the two dimensions of interdependence, mutual dependence and power imbalance. Together, our theorizing and results speak to the driving factors of collaborative success in a context where collaboration is particularly vital.

A case study on software vulnerability coordination

Context: Coordination is a fundamental tenet of software engineering. Coordination is required also for identifying discovered and disclosed software vulnerabilities with Common Vulnerabilities and Exposures (CVEs). Motivated by recent practical challenges, this paper examines the coordination of CVEs for open source projects through a public mailing list.Objective: The paper observes the historical time delays between the assignment of CVEs on a mailing list and the later appearance of these in the National Vulnerability Database (NVD). Drawing from research on software engineering coordination, software vulnerabilities, and bug tracking, the delays are modeled through three dimensions: social networks and communication practices, tracking infrastructures, and the technical characteristics of the CVEs coordinated.Method: Given a period between 2008 and 2016, a sample of over five thousand CVEs is used to model the delays with nearly fifty explanatory metrics. Regression analysis is used for the modeling.Results: The results show that the CVE coordination delays are affected by different abstractions for noise and prerequisite constraints. These abstractions convey effects from the social network and infrastructure dimensions. Particularly strong effect sizes are observed for annual and monthly control metrics, a control metric for weekends, the degrees of the nodes in the CVE coordination networks, and the number of references given in NVD for the CVEs archived. Smaller but visible effects are present for metrics measuring the entropy of the emails exchanged, traces to bug tracking systems, and other related aspects. The empirical signals are weaker for the technical characteristics.Conclusion: Software vulnerability and CVE coordination exhibit all typical traits of software engineering coordination in general. The coordination perspective elaborated and the case studied open new avenues for further empirical inquiries as well as practical improvements for the contemporary CVE coordination.

Using Social Network Analysis to Investigate Positive EOL Communication

ContextEnd-of-life (EOL) communication is a complex process involving the whole family and multiple care providers. Applications of analysis techniques that account for communication beyond the patient and patient/provider will improve clinical understanding of EOL communication. ObjectivesThe objectives of the study were to introduce the use of social network analysis to EOL communication data and to provide an example of applying social network analysis to home hospice interactions. MethodsWe provide a description of social network analysis to model communication patterns during home hospice nursing visits. We describe three social network attributes (i.e., magnitude, directionality, and reciprocity) in the expression of positive emotion among hospice nurses, family caregivers, and hospice cancer patients. Differences in communication structure by primary family caregiver across gender and time were also examined. ResultsMagnitude (frequency) in the expression of positive emotion occurred most often between nurses and caregivers or between nurses and patients. Female caregivers directed more positive emotion to nurses, and nurses directed more positive emotion to other family caregivers when the primary family caregiver was male. Reciprocity (mutuality) in positive emotion declined toward day of death but increased on day of actual patient death. There was a variation in reciprocity by the type of positive emotion expressed. ConclusionOur example demonstrates that social network analysis can be used to better understand the process of EOL communication. Social network analysis can be expanded to other areas of EOL research, such as EOL decision making and health care teamwork.

Analysis of scientific production on interorganizational networks study field

Analysis of scientific production on interorganizational networks study field

Social network evolution in creative process of CNPD teams: a case study of Chinese companies

The existing theories about how social networks affect creativity mostly focus on a certain stage in the organizational life of an idea, for instance, ideas generation, commercialization of ideas, and a previous assumption refers that multiple attributes of social networks of creative team are static. It is not possible to resolve such a realistic and important issue that how dynamic evolution of social networks affects the creative process at the fuzzy front-end of complex new product development (CNPD) of research and development (RD homogeneity of task knowledge, strong-tie network relationship, and centralized network structure are good for idea screening; homogeneity of task knowledge in the network is maintained while new knowledge is obtained actively from the outside; internal network relationship is dominated by strong tie while weak tie is connected with the outside; both diffused and balanced network structures are beneficial to idea development.

Social network size relates to developmental neural sensitivity to biological motion

The ability to perceive others’ actions and goals from human motion (i.e., biological motion perception) is a critical component of social perception and may be linked to the development of real-world social relationships. Adult research demonstrates two key nodes of the brain’s biological motion perception system—amygdala and posterior superior temporal sulcus (pSTS)—are linked to variability in social network properties. The relation between social perception and social network properties, however, has not yet been investigated in middle childhood—a time when individual differences in social experiences and social perception are growing. The aims of this study were to (1) replicate past work showing amygdala and pSTS sensitivity to biological motion in middle childhood; (2) examine age-related changes in the neural sensitivity for biological motion, and (3) determine whether neural sensitivity for biological motion relates to social network characteristics in children. Consistent with past work, we demonstrate a significant relation between social network size and neural sensitivity for biological motion in left pSTS, but do not find age-related change in biological motion perception. This finding offers evidence for the interplay between real-world social experiences and functional brain development and has important implications for understanding disorders of atypical social experience.

Investigating the impact of ceo’s social network on sme performance and access to external resources in the moroccan textile industry

This paper analyses the relationship between the social network dimensions, the performance of Moroccan SMEs of the textile industry and their access to external resources. As these companies face a fierce competition in recent years, their CEOs’ social networks are playing a significant role in their success and survival. Through a sample of 112 SMEs and a quantitative method, our results show that the more the network is important, the more it promotes SME performance and access to information resources, that having closer tie with the bankers allows a better access to financial resources, that knowing and having links with people in high places is a privilege and contributes effectively to an organizational performance.

Social capital and frequent attenders in general practice: a register-based cohort study

BackgroundFrequent attendance to primary care constitutes a large use of resources for the health care system. The association between frequent attendance and illness-related factors has been examined in several studies, but little is known about the association between frequent attendance and individual social capital. The aim of this study is to explore this association.MethodsThe analysis is conducted on responders to the North Denmark Region Health Profile 2010 (n = 23,384), individually linked with information from administrative registers. Social capital is operationalized at the individual level, and includes cognitive (interpersonal trust and norms of reciprocity) as well as structural (social network and civic engagement) dimensions. Frequent attendance is defined as the upper-quartile of the total number of measured consultations with a general practitioner over a period of 148 weeks.ResultsUsing multiple logistic regression, we found that frequent attendance was associated with a lower score in interpersonal trust [OR 0.86 (0.79–0.94)] and social network [OR 0.88 (0.79–0.98)] for women, when adjusted for age, education, income and SF12 health scores. Norms of reciprocity and civic engagement were not significantly associated with frequent attendance for women [OR 1.05 (0.99–1.11) and OR 1.01 (0.92–1.11) respectively]. None of the associations were statistically significant for men.ConclusionThis study suggests that for women, some aspects of social capital are associated with frequent attendance in general practice, and the statistically significant dimensions belonged to both cognitive and structural aspects of social capital. This association was not seen for men. This indicates a multifaceted and heterogeneous relationship between social capital and frequent attendance among genders.

QMSampler: Joint Sampling of Multiple Networks with Quality Guarantee

Because Online Social Networks (OSNs) have become increasingly important in the last decade, they have motivated a great deal of research on Social Network Analysis (SNA). Currently, SNA algorithms are evaluated on real datasets obtained from large-scale OSNs, which are usually sampled by Breadth-First-Search (BFS), Random Walk (RW), or some variations of the latter. However, none of the released datasets provides any statistical guarantees on the difference between the sampled datasets and the ground truth. Moreover, all existing sampling algorithms only focus on sampling a single OSN, but each OSN is actually a sampling of a complete social network. Hence, even if the whole dataset from a single OSN is sampled, the results may still be skewed and may not fully reflect the properties of the complete social network. To address the above issues, we have made the first attempt to explore the joint sampling of multiple OSNs and propose an approach called Quality-guaranteed Multi-network Sampler (QMSampler) that can jointly sample multiple OSNs. QMSampler provides a statistical guarantee on the difference between the sampled real dataset and the ground truth (the perfect integration of all OSNs). Our experimental results demonstrate that the proposed approach generates a much smaller bias than any existing method. QMSampler has also been released as a free download.

Real-time social recommendation based on graph embedding and temporal context

With the rapid proliferation of online social networks, personalized social recommendation has become an important means to help people discover their potential friends or interested items in real-time. However, the cold-start issue and the special properties of social networks, such as rich temporal dynamics, heterogeneous and complex structures, render the most commonly used recommendation approaches (e.g. Collaborative Filtering) inefficient. In this paper, we propose a novel dynamic graph-based embedding (DGE) model for social recommendation which is capable of recommending relevant users and interested items. In order to support real-time recommendation, we construct a heterogeneous user-item (HUI) network and incrementally maintain it as the social network evolves. DGE jointly captures the temporal semantic effects, social relationships and user behavior sequential patterns in a unified way by embedding the HUI network into a shared low dimensional space. Then, with simple search methods or similarity calculations, we can use the encoded representation of temporal contexts to generate recommendations. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its advantages over other state-of-the-art methods.

Hygiene and Health: Who Do Mothers in Vanuatu Communicate with about Health?

Health information-seeking behaviour of mothers with children five years of age and younger in Vanuatu was examined using the structural properties of social networks. Data were collected from a rural village from two islands and an urban settlement in the capital, Port Vila, by face-to-face interviews using a structured questionnaire. Sociometric data on the structure of the network, the characteristics of key informants, and associations with outside sources of health information were analysed as interpersonal predictors of health promotion and behavior change. Rural mothers preferred the health advice of biomedical practitioners for diarrheal disease over traditional custom practitioners. Interpersonal connections were restricted in the urban mother network indicating that mothers were merely acquaintances or do not seek health advice from each other. Our findings suggest that biomedical practitioners are the best option for diffusing health and hygiene information for rural and urban mothers. Traditional healers and paraprofessionals could be strategically used to complete the missing links in network connectedness to optimally spread new information. The novel use of cross-sectional social network data can create a baseline evaluation to purposefully frame a health intervention. Our study provided a unique explanation of how network analysis offers insight into how key players can be identified and the circumstances in which they are likely to be able to influence hygiene practices of their peers.

Utilizing the average node degree to assess the temporal growth rate of Twitter

Several models have been proposed that describe the evolution of the graph properties of many online social networks (OSNs) and explain the behavior of their users. These models are essential for understanding the growth dynamics of the underlying social graph. One of the most prominent OSNs is Twitter, since it covers a significant part of the online worldwide population. Nevertheless, investigating the validity of these models on Twitter entails many difficulties. The size of Twitter and the limitations of its access API make extremely difficult the estimation of many graph properties and therefore the evaluation of the proposed models. In this study, we present a simple and efficient method to fit an already existing model, which describes the densification power law property of modern OSNs. This model states that the average degree of an OSN increases over time. In a case study, we assess this model in two large samples of Twitter, and we demonstrate how it can portray the altering growth periods of Twitter. Finally, we make some remarks on several events during the early period of Twitter that may have affected its growth rates.

A Systematic Review of Research on Social Networks of Older Adults.

There has been a substantial interest in life course/life span changes in older adults' social networks and in the relationship between social networks and health and wellbeing. The study embarked on a systematic review to examine the existing knowledgebase on social network in the field of gerontology. Our focus was on studies in which both ego (respondents) and his or her alters (network members) are queried about their social ties. We searched for studies published in English before September, 2017, relied on quantitative methods to obtain data from both ego (60 years of age and older) and alters and provided a quantitative account of the social network properties. We searched the following data sets: APA Psychnet, Pubmed, Sociological abstracts, and Ageline. This was followed by a snowball search of relevant articles using Google Scholar. Titles and abstracts were reviewed and selected articles were extracted independently by two reviewers. A total of 5,519 records were retrieved. Of these, 3,994 records remained after the removal of duplicates. Ten records reporting on five original samples were kept for the systematic review. One study described a social network of community dwelling older adults and the remaining studies described social networks of institutional older adults. The present study points to a lacuna in current understanding of social networks in the field of gerontology. It provides a useful review and possible tools for the design of future studies to address current shortcomings in the field.

The Use of Online Posts to Identify Barriers to and Facilitators of HIV Pre-exposure Prophylaxis (PrEP) Among Men Who Have Sex with Men: A Comparison to a Systematic Review of the Peer-Reviewed Literature.

Pre-exposure prophylaxis (PrEP) remains an under-utilized HIV prevention tool among men who have sex with men (MSM). To more comprehensively elucidate barriers and facilitators to PrEP use among US MSM, we conducted a systematic review of peer-reviewed published articles and content analysis of online posts about PrEP. We searched peer-reviewed databases (Medline, Web of Science, Google Scholar) using MESH headings and keywords about PrEP and/or HIV prevention from 2005 to 2015. We included original studies among MSM in the US that reported on barriers, facilitators, or other factors related to PrEP use. We also searched online posts and associated comments (news articles, opinion pieces, blogs and other social media posts) in diverse venues (Facebook, Slate Outward, Huffington Post Gay Voices, Queerty, and My PrEP Experience blog) to identify posts about PrEP. We used content analysis to identify themes and compare potential differences between the peer-reviewed literature and online posts. We identified 25 peer-reviewed articles and 28 online posts meeting inclusion criteria. We identified 48 unique barriers and 46 facilitators to using PrEP. These 94 themes fit into six overarching categories: (1) access (n=14), (2) attitudes/beliefs (n=24), (3) attributes of PrEP (n=13), (4) behaviors (n=11), (5) sociodemographic characteristics (n=8), and (6) social network (n=6). In all categories, analysis of online posts resulted in identification of a greater number of unique themes. Thirty-eight themes were identified in the online posts that were not identified in the peer-reviewed literature. We identified barriers and facilitators to PrEP in online posts that were not identified in a systematic review of the peer-reviewed literature. By incorporating data both from a systematic review of peer-reviewed articles and from online posts, we have identified salient and novel information about barriers to and facilitators of PrEP use. Traditional research approaches may not comprehensively capture current factors important for designing and implementing PrEP related interventions.

Is Social Network a Protective Factor for Cognitive Impairment in US Chinese Older Adults? Findings from the PINE Study

Background: Social network has been identified as a protective factor for cognitive impairment. However, the relationship between social network and global and subdomains of cognitive function remains unclear. Objective: This study aims to provide an analytic framework to examine quantity, composition, and quality of social network and investigate the association between social network, global cognition, and cognitive domains among US Chinese older adults. Methods: Data were derived from the Population Study of Chinese Elderly (PINE), a community-engaged, population-based epidemiological study of US Chinese older adults aged 60 and above in the greater Chicago area, with a sample size of 3,157. Social network was assessed by network size, volume of contact, proportion kin, proportion female, proportion co-resident, and emotional closeness. Cognitive function was evaluated by global cognition, episodic memory, executive function, working memory, and Chinese Mini-Mental State Examination (C-MMSE). Linear regression and quantile regression were performed. Results: Every 1-point increase in network size (b = 0.048, p < 0.001) and volume of contact (b = 0.049, p < 0.01) and every 1-point decrease in proportion kin (b = -0.240, p < 0.01) and proportion co-resident (b = -0.099, p < 0.05) were associated with higher level of global cognition. Similar trends were observed in specific cognitive domains, including episodic memory, working memory, executive function, and C-MMSE. However, emotional closeness was only significantly associated with C-MMSE (b = 0.076, p < 0.01). Social network has differential effects on female versus male older adults. Conclusion: This study found that social network dimensions have different relationships with global and domains of cognitive function. Quantitative and structural aspects of social network were essential to maintain an optimal level of cognitive function. Qualitative aspects of social network were protective factors for C-MMSE. It is necessary for public health practitioners to consider interventions that enhance different aspects of older adults' social network.

Estimating country-level social network density and supportive surroundings by simulation

The purpose of this study is to estimate country-level social network properties by reproducing plausible social network structures of each country. For this purpose, we suggest and utilize a novel ...