Application Of Social Network Analysis Research Articles

Vocational Rehabilitation Service Patterns: An Application of Social Network Analysis to Examine Employment Outcomes of Transition-Age Individuals With Autism

Young adults with autism spectrum disorder (ASD) face poor employment outcomes following transition from school to adult life. Social network analysis is a useful approach for examining service patterns associated with employment success for this population. An advantage of this approach is its focus on the interdependence of variables rather than individual predictors. This study applies network methodology to examine the relations between vocational rehabilitation services and young adults with ASD to predict employment status. Using the Rehabilitation Services Administration (RSA-911) data set, participants included 2,219 individuals with ASD between the ages of 16 and 24 served by the public vocational rehabilitation system and closed as either competitively employed or not employed. A two-mode network was constructed such that a relation was defined for each service an individual received. Results from a core-periphery analysis indicated that of the 22 services available, core services included assessment, counseling/guidance, job placement, on-the-job support, job search support, and transportation services. Follow-up analyses suggested that the greater number of these six core services an individual received, the better the employment outcome. Findings highlight that these services should be viewed as interconnected and suggest a set of six core services that may be particularly beneficial for this population.

Application of Social Network Analysis in Diverse Health and Allied Disciplines –A Review of Existing Research Literature

Application of Social Network Analysis in Diverse Health and Allied Disciplines –A Review of Existing Research Literature

Mapping a Careflow Network to assess the connectedness of Connected Health.

Connected Health is an emerging and rapidly developing field which has the potential to transform healthcare service systems by increasing its safety, quality and overall efficiency. From a healthcare perspective, process improvement models have mainly focused on the static workflow viewpoint. The objective of this article is to study and model the dynamic nature of healthcare delivery, allowing us to identify where potential issues exist within the service system and to examine how Connected Health technological solutions may support service efficiencies. We explore the application of social network analysis (SNA) as a modelling technique which captures the dynamic nature of a healthcare service. We demonstrate how it can be used to map the 'Careflow Network' and guide Connected Health innovators to examine specific opportunities within the healthcare service. Our results indicate that healthcare technology must be correctly identified and implemented within the Careflow Network to enjoy improvements in service delivery. Oftentimes, prior to making the transformation to Connected Health, researchers use various modelling techniques that fail to identify where Connected Health innovation is best placed in a healthcare service network. Using SNA allows us to develop an understanding of the current operation of healthcare system within which they can effect change. It is important to identify and model the resource exchanges to ensure that the quality and safety of care are enhanced, efficiencies are increased and the overall healthcare service system is improved. We have shown that dynamic models allow us to study the exchange of resources. These are often intertwined within a socio-technical context in an informal manner and not accounted for in static models, yet capture a truer insight on the operations of a Careflow Network.

Social network analysis in sport research: an emerging paradigm

While network analysis is a major methodological approach in many disciplines of the social and natural sciences, it has only recently come into the focus of sport researchers. This article assesses the utility of network analysis to analyze sport phenomena. We begin with an overview of social network analysis (SNA) and related concepts. To explore research topics and approaches, we conduct a systematic review of empirical literature of SNA and its application to sport. Based on this review, we provide a six-dimensional conceptual typology of SNA applications in sport – competition networks, interaction networks, inter-organizational networks, intra-organizational networks, affiliation networks and social environments. Potential future directions for this promising approach in sport research are discussed.

Applications of social network analysis in behavioural information security research: Concepts and empirical analysis

The rapid digital transformation and technological disruption in modern organisations demand the development of people-centric security workplaces, whereby the employees can build up their security awareness and accountability for their actions via participation in the organisation's social networks. The social network analysis approach offers a wide array of analytical capabilities to examine in-depth the interactions and relations within an organisation, which assists the development of such security workplaces. This paper proposes the novel and practical adoption of social network analysis methods in behavioural information security field. To this end, we discuss the core features of the social network analysis approach and describe their empirical applications in a real case study of a large organisation in Vietnam, which utilised these methods to improve employees' information security awareness. Towards the end of the paper, a framework detailing the strategies for conducting social network analysis in the behavioural information security field is developed and presented.

015 Do pigs form social structures: an application of social network analysis?

015 Do pigs form social structures: an application of social network analysis?

Social networks in the context of community response to disaster: Study of a cyclone-affected community in Coastal West Bengal, India

The strength and effectiveness of social networks influence the ability of communities to cope with disaster events. Social Network Analysis (SNA) provides scope of analysing such complex networks in disaster-hit communities. We describe the application of SNA in a disaster-hit community and show the changing pattern of evolving networks during and after the disaster. The disaster event was conceptually divided into four distinct phases namely ‘extreme event’ (Phase-1), ‘immediate community response’ (Phase-2), ‘relief’ (Phase-3) and ‘rehabilitation’ (Phase-4), through a series of focus group discussions with the community. We also considered the Pre- and Post-disaster phases for before-after comparison of the community's social network. Network data for all these six phases was collected through personal interview from the affected households located besides the river embankment. For all the six phases, unique networks were found with different central nodes, although few nodes remained central in more than one the phase. Different measures of network density and mean network centrality increased from the pre-disaster stage in Phase-1, just after the disaster event, and then consistently reduced from Phase 2 to Phase 4. Then again they increased at the post-disaster phase. While the Phase-1 was characterized by endogenous nodes and ties, during the later stages, the networks assumed a core central structure constituted of both internal and external nodes, with peripheral components. The internal and external central actors maintained link between local (friends, relatives, neighbours) and external (institutional) entities. The analysis illustrates the interactions within and between community networks, and may initiate situational awareness, efficient planning, and optimal resources allocation for disaster preparedness, community resilience, and response.

((( 195441957 ) (Analysis of Social Networks in the Historical Sociology of Culture (On the Basis of Soviet Children's Literature of 1954-1957))

Russian Abstract: Предлагаемая работа обосновывает применимость метода анализа социальных сетей (social networks analysis) в исторической социологии литературы. В новейших исследованиях по социологии культуры основное внимание обращается не на социальные связи между отдельными авторами, редакторами, переводчиками или читателями (social networks), а на сети коммуникаций в литературных сообществах или сети трансляции текстов (по аналогии с social media в Интернете). С нашей точки зрения, коммуникация – лишь частный случай социального взаимодействия в сетевых структурах, изучение которых позволяет увидеть принципиально новые аспекты создания и рецепции литературных произведений. Предмет исследования в этой работе – резкая трансформация социальных связей между детскими писателями, критиками и литературными институциями в 1954—57 годах, приведшая к появлению «оттепельных» направлений в детской литературе. Изучение этого частного случая помогает увидеть более общие закономерности институционального и «сетевого» развития (networking) позднесоветской культуры и понять специфику советских сетевых структур по сравнению с изученными в англоязычной социологической литературе. English Abstract: This paper is aimed at grounding of applicability of social network analysis to historical sociology of literature. In modern studies in cultural sociology, the scholars’ attention is paid mostly not to social networks that connect authors, editors, translators, and readers, but to the communication channels or the networks for texts’ widespread – in other words, to the phenomena, resembling the social media on the Internet. In our opinion, communication here is only particular case in the wide range of social interactions in literary communities. Study of these communities from the viewpoint of social network analysis allows to discern the new aspects of literary works’ production and reception, and to shed new light on the well-known episodes of literary history. The subject of the case study in this paper is the quick transformation of the social ties between the Soviet children’s writers, critics, and literary institutions in 1954-57. This transformation led to the total reshaping of landscape of the Soviet children’s literature. This case study gives ground for consideration the deeper principles that regulated networking and institutional dynamics of the late-Soviet culture, as well as the specific features of the Soviet cultural networks if comparing them with the samples discussed in English-language sociological scholarship.

Shifting Alliances in International Organizations: A social networks analysis of co-sponsorship of UN GA resolutions, 1976-2012

While general belief is that the military alliances are stable and rigid, the authors argue that the states are far more flexible in their behavior and often act against their alliances. This paper looks at states' behavior in the UN GA and looks how it is reflected in participation in military alliances during three periods of history since 1976 to this day. The authors illustrate the need to consider the network representation of co-sponsoring groups in General Assembly votes. They find significant support for their argument, indicating that social aspects can be extended beyond alliances. An application of social network analysis shows some unexpected affiliations in UN GA. If the UN GA is the "true" nature of these countries' alliance strategies, then it might suggest some significant defections and interesting association.

Network centrality based team formation: A case study on T-20 cricket

This paper proposes and evaluates the novel utilization of small world network properties for the formation of team of players with both best performances and best belongingness within the team network. To verify this concept, this methodology is applied to T-20 cricket teams. The players are treated as nodes of the network, whereas the number of interactions between team members is denoted as the edges between those nodes. All intra country networks form the cricket network for this case study. Analysis of the networks depicts that T-20 cricket network inherits all characteristics of small world network. Making a quantitative measure for an individual performance in the team sports is important with respect to the fact that for team selection of an International match, from pool of best players, only eleven players can be selected for the team. The statistical record of each player considered as a traditional way of quantifying the performance of a player. But the other criteria such as performing against a strong opponent or performance as an effective team member such as fielding, running between the wickets, good partnership deserves more credential. In this paper a revised method based on social networking is presented to quantify the quality of team belongingness and efficiency of each player. The application of Social Network Analysis (SNA) is explored to measure performances and the rank of the players. A bidirectional weighted network of players is generated using the information collected from T-20 cricket (2014–2016) and used for network analysis. Thus team was formed based on that ranking and compared with their IPL (Indian Premier League) performances of 2016.

So that’s what the impact of IT innovation looks like? Examining the socio-technical dynamics of public service innovation

PurposeServices comprise of socio-technical (human and technological) factors which exchange various resources and competencies. Service networks are used to transfer resources and competencies, yet they remain an underexplored and “invisible” infrastructure. Considering the growth in technological investment in recent years, this research sets out to model the impact of IT-enabled innovation on a service network. In response to the growing importance placed on understanding these complexities, the field of “service science” has emerged to guide the effective design, implementation, and management of service systems. The purpose of this paper is to investigate the impact of introducing an IT-enabled innovation in a public service network.Design/methodology/approachThis is achieved through a case study of an Exam Administration Service Department (EASD) where an electronic grading system was introduced to improve the EASD grading process. Data are analysed using both actor-network theory (ANT) as a theoretical lens and social network analysis (SNA) for empirical purposes to visualise the impact of IT-enabled innovation on a service environment.FindingsThe research described in this paper makes a useful contribution to the service science and IT innovation community both in terms of its topic (public service networks) and in terms of its theoretical framework and application methods (ANT and SNA).Originality/valueThis paper demonstrates how we can investigate the impact of IT-enabled innovation within a service network. Most notably, the application of SNA enables us to visualise the impact of technology and gain insights on the socio-technical dynamics associated with introducing service innovations.

Social network analysis in supply chain management research

Purpose The purpose of this paper is to investigate when and how to best use social network analysis (SNA) in the supply chain management (SCM) discipline. In doing so, the study identifies SCM phenomena that have been examined from a social network perspective (SNA approach) in the SCM literature and highlights additional SCM phenomena that would be worth investigating using social network research. Then, the study critically investigates the application of SNA as a methodology (SNA method), with the goal of assessing and mitigating methodological risks in future studies. Design/methodology/approach This study carries out a systematic literature review of articles published in 11 top-tier SCM journals over a 20-year period. Findings First, while social network research has gained momentum especially since 2010, scholars are not yet entirely aware of the many possibilities the SNA approach offers to the SCM field. Second, expanded possibilities also hold for the development of SNA as a method. Originality/value The paper guides future SCM research by investigating when SNA is the right approach to use and how SNA as a method should be performed. Theoretically richer and practically more relevant research should result.

Visualizations of Online Course Interactions for Social Network Learning Analytics

Social network learning analytics aims to extract useful information to improve the learning process, but the variety of learning management systems makes this task burdensome and difficult to manage. This study shows how Gephi, a general-purpose, open-source social network analysis application, can be used by instructors and institutions to extract and visualize relevant information that is commonly hidden or difficult to observe for course coordinators and teachers. The empirical case study uses data from one cross-curricular course with 656 students at the Open University of Catalonia (UOC) and showcases the use of Gephi as a social network learning analytics tool. The study further discusses the potential of social network learning analytics to improve online instruction by visualization of educational data.

Review of the application of social network analysis (SNA) in construction project management research

Over the past two decades, social network analysis (SNA) has elicited increasing attention in construction project management (CPM) research as a response to the emerging perspective of viewing projects as network-based organizational organizations. However, a thorough review of SNA application in CPM research is unavailable. This study aims to address this gap by reviewing 63 SNA papers published in eight peer-reviewed journals from 1997 to 2015 to ascertain the status of this research area and identify future research directions. The papers are analyzed in terms of institutional and individual contribution, citations, topic coverage and research design and methodologies. Three research directions, namely, internal stakeholder networks for outcome-related values, external stakeholder networks for process-related values, and external stakeholder networks for outcome-related values, are identified. The findings of this study are believed to provide useful references for the future application of SNA in CPM research.

An analysis of patient-sharing physician networks and implantable cardioverter defibrillator therapy.

The application of social network analysis to the organization of healthcare delivery is a relatively new area of research that may not be familiar to health services statisticians and other methodologists. We present a methodological introduction to social network analysis with a case study of physicians' adherence to clinical guidelines regarding use of implantable cardioverter defibrillators (ICDs) for the prevention of sudden cardiac death. We focus on two hospital referral regions (HRRs) in Indiana, Gary and South Bend, characterized by different rates of evidence-based ICD use (86% and 66%, respectively). Using Medicare Part B claims, we construct a network of physicians who care for cardiovascular disease patients based on patient-sharing relationships. Approaches for weighting physician dyads and aggregating physician dyads by hospital are discussed. Then, we obtain a set of weighted network statistics for the positions of hospitals in their referral region, global statistics for the physician network within each hospital, and of the network positions of individual physicians within hospitals, providing the mathematical specification and sociological intuition underlying each measure. We find that adjusting for network measures can reduce the observed differences between referral regions for evidence-based ICD therapy. This study supports previous reports on how variation in physician network structure relates to utilization of care, and motivates future work using physician network measures to examine variation in evidence-based medicine.

Community Interagency Connections for Immigrant Worker Health Interventions, King County, Washington State, 2012–2013

IntroductionCross-sector community partnerships are a potentially powerful strategy to address population health problems, including health disparities. US immigrants — commonly employed in low-wage jobs that pose high risks to their health — experience such disparities because of hazardous exposures in the workplace. Hazardous exposures contribute to chronic health problems and complicate disease management. Moreover, prevention strategies such as worksite wellness programs are not effective for low-wage immigrant groups. The purpose of this article was to describe an innovative application of social network analysis to characterize interagency connections and knowledge needed to design and deliver a comprehensive community-based chronic disease prevention program for immigrant workers.MethodsUsing iterative sample expansion, we identified 42 agencies representing diverse community sectors (service agencies, faith-based organizations, unions, nonprofits, government agencies) pertinent to the health of Chinese immigrant workers. To capture data on shared information, resources, and services as well as organizational characteristics, we jointly interviewed 2 representatives from each agency. We used social network analysis to describe interagency network structure and the positions of agencies within the networks.ResultsAgency interconnections were established primarily for information sharing. In the overall interagency network, a few service-oriented agencies held central or gatekeeper positions. Strong interconnectedness occurred predominately across service, public, and nonprofit sectors. The Chinese and Pan-Asian service sectors showed the strongest interconnectedness.ConclusionNetwork analysis yields critical understanding of community structural links and assets needed to inform decisions about actual and potential community collaborations. Alternative intervention strategies may be needed to address health disparities among immigrant workers.

INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

With the proliferation of graph applications in social network analysis, biological networks, WWW and many other areas, a great demand of efficient and scalable algorithms for graph mining is rising. In many applications, finding the most influential nodes in the network is informative for the network analyzers in order to track the spread of information, disease and rumors. The problem of finding the top k influential nodes of a directed graph G=(V,E) such that the influence spread of these nodes will be maximized has long been exposed and many algorithms have been proposed to deal with this problem. Despite the useful characteristics of community structure in social networks, only a few works have studied the role of communities in the spread of influence in social networks.In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs.

Simultaneous optimization for robust correlation estimation in partially observed social network

Correlation estimation plays a critical role in numerous applications of social network analysis. Traditionally, the numerical records of the interaction between users are used as quantitative metrics of correlation. The deficiencies are threefold. Firstly, a single source of interaction is far from sufficient to reveal the underlying correlation. Secondly, the data available are often partially observed result from the imperfection in data acquisition and storage techniques, thereby jeopardizing the reliability of estimation. Thirdly, the inference from the explicit features to the implicit correlation is far from straightforward. Simply taking interaction as correlation is neither theoretically nor practically plausible. The former issue can be addressed via matrix completion, whereas the latter is essentially a self-expressive matrix representation problem. Instead of solving the two problems separately, in this paper, we propose a simultaneous optimization algorithm for robust correlation estimation based on partially observed data. In this way, the global, rather than local, optima can be achieved in an effective manner. The experiments on both synthetic and real-world social network data demonstrate the advantage of the proposed method.

Social network analysis and social capital in marketing: theory and practical implementation

The application of social network analysis in marketing can provide marketers with valuable insights for developing communication and branding strategies by building up social capital in social networking sites (SNSs) as Facebook. In this paper, the main characteristics and definitions of social capital and social network analysis (SNA) are reviewed and discussed, including the importance of social capital for marketing and branding in SNSs. SNA is used to analyse the network structure of a famous Greek brand page, and the activity (comments) of its members during a period of two months in 2013. The interactions between consumers, the content generated by the firm and the posts commenting on this content are presented and analysed in terms of SNA and marketing, highlighting the importance of content creation in attracting users to like and comment increasing engagement. The implications and applications of the use of SNA results and of social capital as a way of building brand loyalty in marketing decisions and planning are discussed, and suggestions for further research are offered.

A new learning automata‐based sampling algorithm for social networks

SummaryRecently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA‐based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well‐known sampling algorithms in terms of relative error and Kolmogorov–Smirnov test. It is shown that eDLA‐based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA‐based sampling algorithm in comparison with the DLA‐based sampling algorithm has a 26.93% improvement for the average of Kolmogorov–Smirnov value for degree distribution taken over all test networks. Copyright © 2015 John Wiley & Sons, Ltd.