Application Of Social Network Analysis Research Articles

Relationally integrated value networks for total asset management in project portfolios

Relationally integrated value networks for total asset management (Rivans-Tam) have been recommended by scholars as a vehicle to integrate the stakeholders of project management (PM) and of asset management (Am), covering the entire life cycle of a built asset. This paper presents a scoping and exploratory pilot study to test a further proposition that expands the basic Rivans-Tam concept to encompass portfolio management, to benefit asset-intensive organisations holding a large portfolio of ongoing construction projects and operational built assets. Accordingly, it establishes a strong theoretical underpinning based on previously validated research exercises on Rivans-Tam and previous applications of social network analysis (SNA) for Rivans-Tam and developed from in-depth high-level semi-structured interviews with six industry professionals with relevant expertise and experience. The outcomes broaden the theoretical base of Rivans-Tam, also highlighting its practical potential to empower total portfolio management (TPM). This TPM perspective expands the hitherto ‘single asset’-focused Rivans-Tam concept and related SNA methods to a wider portfolio of both ongoing construction projects and operational built assets. Moreover, the consolidated findings contribute substantially to practice by providing a basis to overcome the main challenges and unlock the potential synergies from ‘overall best value’-focused relational integration of hitherto disconnected diverse stakeholders.

Methodological aspects around the reconstruction of surname sub-sets in bimodal networks

This article addresses the methodological aspects surrounding the transformation of a cadastral land registry from the end of the 19th century belonging to the department of Humahuacua, Jujuy province (Argentina), in a series of bimodal matrices and networks. The idea that has guided the relational treatment of the data towards said historical sources, starts from the premise that certain individuals carrying a discrete set of surnames, and who have shared similar monetary amounts of appraisal declared in the said land cadastre, were more likely to link with each other in different areas of social life, such as the limited political and regional power circle of the late nineteenth century in Jujuy. The results obtained show the possibility of delimiting and reconstructing sub-sets of surnames that are linked less and/or more frequently to each other, according to four pre-constructed appraisal categories based on a segmentation of the registered monetary valuations in the land registry. Likewise, and as a product of this new grouping of information arising from the application of Social Network Analysis (ARS), it is feasible to limit the study to the qualitative aspects (age, residence, descent, nuptial ties, baptismal sponsorships) of individuals that formed part of these resulting sub-sets, through the focused search of ecclesiastical acts recorded within the same historical period.

When links build networks: brief history about the Antropocaos Group

The Antropocaos Group of the Universidad de Buenos Aires has been working on topics related to Social Network Analysis and other complexity algorithms for more than 20 years. This article reviews some of the team’s productions lines of work and the more frequent methodological choices. To this end, we outlined a brief history of our formation in the university cloisters, from the beginning of the 1990s to the present day. We emphasize the empirical topics addressed by the group, in order to highlight both the conceptual variability and the methodological unity regarding different areas of Social Network Analysis application. A section is dedicated specifically to those studies where we implemented simulation models and the possibility to observe reticular phenomena in a controlled computational environment. The next section shows different areas in which the members of Antropocaos currently carry out their activities and apply reticular methods, including academic research, management, and public administration, and different sorts of consultancies. Finally, we point toward some guidelines about the future of the group, as well as the lines of research that are just being started, related to algorithms of Artificial Intelligence, as well as our long-standing commitment to disseminate the Social Networks Analysis methodology.

An ontology-based multi-domain model in social network analysis: Experimental validation and case study

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.

Italian tourism intermediaries: a social network analysis exploration

ABSTRACT The purpose of this paper is to analyse the application of Social Network Analysis (SNA) to the Italian tourism system. The research question is: do relationships among tourist enterprises affect the organizational asset of the Italian travel system? The research takes as unit of analysis the Italian travel agencies and tour operators system and represents quite a significant disclosure for organizational theses because it offers a different view over the structure and governance of a hospitality intermediaries’ network. SNA is helpful indetecting genuine proficiency and therefore in foreseeing possible losses determined by poor or inefficient configurations. Furthermore, it will help delineate new roles within the organizational networks and evaluate the relation between formal and informal organizational structures. This paper provides a structural analysis of the Italian travel agencies network and highlights its self-organization characteristics (typical of a complex system) that lead to the development of informal communities. The methods of network science proved useful and effective and, together with more traditional approaches and a qualitative knowledge of the system, can provide a deeper and more extensive understanding of the system.

Preventing Internal COVID-19 Outbreaks within Businesses and Institutions: A Methodology Based on Social Networks Analysis for Supporting Occupational Health and Safety Services Decision Making

This study aims at developing and demonstrating in a real case study a methodology for supporting Occupational Health and Safety Services in the design and assessment of preventive measures to reduce the risks of COVID-19 outbreaks within their entities. The proposed methodology applies the concepts from Social Network Analysis (SNA) to the current challenge of preventing risks of contagion of viruses like SARS-COV-2 among employees. For this purpose, the authors consider a network of employees whose interaction is caused by triggers, which are defined as common circumstances between two workers that may result in contagion, like sharing an office or participating in the same management board. The network cohesion is then evaluated, and those core nodes, which are the most significant contributors to its integration, are identified to be addressed in the design of the preventive measures. The impact of the designed preventive measures on the networks’ cohesion is assessed for its prioritization and further deployment. The methodology has been demonstrated in a real case, a Spanish Research Center, providing promising results in a quick and easy manner. The objective insights provided by its application were demonstrated as very valuable for the Occupational Health and Safety Services in the design and evaluation of the set of preventing measures to be implemented before the return of the employees to the facilities after the Spanish confinement period. The current COVID-19 outbreak brings the need to develop tools and methods to support businesses and institutions in the use of SNA for preventing outbreaks among their employees. Although some literature does exist in the field of SNA application in epidemiology, its adaptation for extensive use by the Occupational and Health Services is still a challenge.

Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter?

The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students’ learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students’ participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.

Exploring the Risk Factors of Infrastructure PPP Projects for Sustainable Delivery: A Social Network Perspective

Due to the relatively long period and large capital flow of public-private partnership (PPP) projects, PPP participants are faced with a complex risk situation impeding the sustainable project delivery. In recent years, risk management of PPP projects has received increasing attention. In this paper, twenty risk factors associated with infrastructure PPP projects were identified by literature review and in-depth case studies. Relationship data for these twenty typical risk factors were obtained through structured interviews. Based on the obtained data, the risk relationship network within infrastructure PPP projects was identified, and the network structure characteristics were analyzed, including individual node attributes and the influence and cohesion of subgroups. The results indicate that key risk factor nodes can form a reaction chain via bridge nodes that can trigger a risk domino effect within PPP projects. Specifically, the key risk factors of PPP projects are divided into two categories, the first of which include risk factors that have powerful and independent influence, such as delay in government approval, government credit, and imperfect legal and regulatory systems. The second category includes risk factors that are highly vulnerable and easily influenced, such as completion risks, insufficient revenue in the market, and fee change. A key risk factor reaction chain is one in which legal change leads to a decline in government credit rating, triggering a contract risk. Twelve bridge nodes were identified that play an important intermediary role in the network, e.g., legal change, public objection, and financing risk. This paper extends the application of social network analysis in PPP projects management research and identifies the key risk factors and crucial factors influencing chain reactions in PPP projects. The results provide a more in-depth understanding of sustainable PPP project management for government agencies and private enterprises.

Patterns of peer scaffolding in technology-enhanced inquiry classrooms: application of social network analysis

The purpose of this study was to identify types and patterns of peer scaffolding that occur during inquiry-based learning (IBL) group activities. It employed a single instrumental case approach that integrated quantitative and qualitative analyses of data gathered from 21 students in a ninth grade biology course. A verbal analysis, a content analysis, and a social network analysis (SNA) were performed to identify patterns in group interactions and refine emergent themes. First, nine types of peer scaffolding were identified and found to serve the goals of direction maintenance, cognitive structuring, and simplification. Second, three different patterns related to the high, mixed, and low prior knowledge levels of each group were identified. The high prior knowledge group provided peer scaffolding that focused attention on considerations key to developing their arguments, and this scaffolding may have improved the group’s work. In the mixed prior knowledge group, the students with greater prior knowledge were likely to support those with less prior knowledge. Together, these findings indicate the way students are grouped may impact observable patterns in peer scaffolding. Identifying the difficulties that learners face and the assistance they seek could help instructional designers and teachers identify areas in which students need support during IBL group activities. This study informs educators and practitioners of effective strategies for designing and implementing peer scaffolding to assist inquiry activities in technology-enhanced classroom settings.

Cross-national digital humanities research collaborations: structure, patterns and themes

PurposeThe purpose of this paper is to examine the structure, patterns and themes of cross-national collaborations in Digital Humanities research through the application of social network analysis and visualization tools.Design/methodology/approachThe sample includes articles containing Digital Humanities research in the Web of Science Core Collection as of December 2018. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators and international communities were calculated. Third, the research topics of different cross-national collaboration communities based on ISI keywords, author keywords, title and abstracts were detected.FindingsThe results show that the scope of international collaborations in Digital Humanities research is broad, but the distribution among nations is unbalanced. The USA, Germany and England were identified as the major contributors. Five research communities are identified, led by the USA, Germany, England, Belgium and France. The communities share common research topics such as history, GIS, text mining, visualization, while each has its own research emphasis.Originality/valueThis study applied various informetric methods and tools to reveal the collaboration structure, patterns and themes among nations in Digital Humanities research.

The Los Angeles Diabetes Prevention Coalition Experience: Practical Applications of Social Network Analysis to Inform Coalition Building in Chronic Disease Prevention.

The Los Angeles Diabetes Prevention Coalition Experience: Practical Applications of Social Network Analysis to Inform Coalition Building in Chronic Disease Prevention.

Semantic analysis on social networks: A survey

SummaryAs social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

Understanding Interactions between Design Team Members of Construction Projects Using Social Network Analysis

Social network analysis (SNA) has not been used to study design project teams in which the full interactions have become more complex (formal and informal) because the team members are from different companies and there is no colocation. This work proposes a method to understand the interactions in the design teams of construction projects using SNA metrics and the sociograms generated within temporary organizations. This study includes three stages: (1) a literature review of the dimensions of interactions within work teams and the application of SNA to the architecture, engineering, and construction (AEC) industry; (2) a proposal of an interaction network method for construction project design teams; and (3) an analysis of a pilot project. Interaction networks were defined in two categories: general interactions and commitment management. For each network, metric indicators were defined for the analysis. The pilot project showed high levels of consistency among team responses. The proposed method allows an analysis of the entire work team and of each individual team member. The method also makes it possible to analyze the work team from a global perspective by carrying out a joint analysis of two or more networks.

Social Network Analysis in Software Development Projects: A Systematic Literature Review

Software development in project teams has become more and more complex, with increasing demands for information and decision making. Software development in projects also hugely depends on effective interaction between people, and human factors have been identified as key to successful software projects. Especially in this context, managing and analyzing social networks is highly important. The instrument of social network analysis (SNA) provides fine-grained methods for analyzing social networks in project teams, going beyond the traditional tools and techniques of project management. This paper examines the importance of the application of SNA in software development projects. We conducted a systematic literature review (SLR) of research on software development projects and social network data published between 1980 and 2019. We identified and analyzed 86 relevant studies, finding that research on software development projects spans the topics of project organization, communication management, knowledge management, version and configuration management, requirement management, and risk management. Further, we show that most studies focus on project organization and that the most common method used to gather social data relies on automated extraction from various software development repositories in the SNA context. Our paper contributes to the software development literature by providing a broad overview of published studies on the use of social networks in helping software development projects. Finally, we identify research opportunities and make suggestions for addressing existing research gaps.

Asymmetric Social Comparison and Life Satisfaction in Social Networks

The aim of this study is to understand the effect of asymmetric social comparison on subjective well-being, and how it differs due to reciprocity. Our approach considers the social network of individuals as a local reference group. We tested competing hypotheses on negative and positive effects of comparison with worse-off (downward) and better-off (upward) targets with a representative sample of 1596 Chileans over the age of 18. The findings support that life satisfaction is influenced by social comparison. By considering the social network as a reference group, the positive effect of downward comparison and the negative effect of upward comparison are confirmed. Upward comparison seems to be more substantial than downward comparison. Additionally, the positive effect of downward comparison decreases slightly with a reciprocal exchange of support between respondents and targets. The application of social network analysis opens a path to understanding the mechanisms underlying social comparison processes.

Towards a systematic description of the field using keywords analysis: main topics in social networks

This paper presents the results of the analysis of keywords used in Social Network Analysis (SNA) articles included in the WoS database and main SNA journals, from 1970 to 2018. 32,409 keywords were obtained from 70,792 works with complete descriptions. We provide a list of the most used keywords and show subgroups of keywords which are connected to each other. To go deeper, we place the keywords into the contexts of selected groups of authors and journals. We use temporal analysis to get an insight into some keyword usage. The distributions of the number of keyword types and tokens over time show fast growth starting from 2010s, which is the result of the growth in the number of articles on SNA topics and applications of SNA in various scientific fields. Even though the most frequently used keywords are trivial or general, the approaches used for the normalization of network link weights allow us to extract keywords representing substantive topics and methodological issues in SNA.

Endogenous Political, Institutional, Cultural, and Geographic Determinants of Intermunicipal Cooperation—Evidence from Slovakia

All over Europe, but especially in post-communist countries, the institutional environment has been undergoing major changes. In Slovakia, regaining their autonomy has led local governments on the path of fragmentation, unsustainably high expenditures for the provision of public services, and an increase in transaction costs. Current policies targeting these issues are heavily focused on intermunicipal cooperation (IMC). Based on four case studies of different institutional arrangements, this paper aims to investigate which endogenous political, institutional, cultural, and geographic factors influence cooperation among Slovak municipalities. Through the application of social network analysis and regression analysis, we reached several relevant conclusions. A number of common assumptions were confirmed, namely that population size and heterogeneity play a major role, but also that the impact of political affiliation as a deciding factor of IMC is not as straightforward as previous evidence suggested. Results also underline the importance of cross-sectoral partnerships such as the EU’s LEADER initiative as a viable alternative to more traditional forms of IMC (but with some limitations).

Proximity patterns in water buffaloes' activities on pasture.

Water buffaloes are considered social animals and perform several activities on pasture, such as grazing, moving, standing, ruminating, wallowing, lying, and drinking. However, the way these animals form their social structure in the herd during each one of these activities is still unknown. Literature for water buffaloes has focused mainly on their productive characteristics, impact of grazing on wetlands and behavior during grazing but failed to address the way these animals form their social organization during their activities on pasture. In this study, the tools of social network analysis are used to analyze, detect, and depict the proximity patterns in water buffaloes' activities on pasture and the effect of their age and gender on them. We describe and interpret a series of global and local network indices, and show that the water buffaloes differentiate their social structure in their activities on pasture and that the animals' age and gender affect their interacting patterns, and provide a framework for the application of social network analysis on grazing animals' social behavioral studies. We expect that this framework could be used in future research to provide information regarding the social structure of other kinds of animals that graze in different forage and climatic environments and help animal breeders to improve their management practices.

Global trends and status in landfilling research: a systematic analysis

Despite the increasing amount of waste being reused, recycled, or energetically valorized, landfills continue to play an important role in waste management strategies. Many researchers have studied various aspects of landfilling, regarding positive or negative features. However, a few of them have summarized the related literature, resulted in a lack of overall progress understanding. This paper aimed to perform a bibliometric analysis of landfilling research during 2000–2017. Various aspects, such as document types, languages, major journals, key countries, authors, and keywords were all systematically analyzed. The collaborations among authors and countries were wholly constructed, visualized, and evaluated through the application of social network analysis based on co-authorship relations. Moreover, to discover the most leading topics on landfill discussion, keyword clustering analysis was conducted using co-occurrences relations. The research showed that the number of landfill-related publications has significantly increased within the period. Regarding both quality and quantity, the United States was the most leading country. As well, Rowe, R.K., from Canada, was quantitatively the most productive author. The findings of this study indicated that landfill leachate was the most studied topic in the literature. Besides, the keywords “global warming” and “sustainability” had the highest rate of growth during the studied period. This paper also demonstrated that the most productive countries in landfill research have had a significant contribution to decrease the total amount of landfilling using other waste disposal alternatives (such as incineration with/without energy recovery) or waste management strategies.

Constructing the social network prediction model based on data mining and link prediction analysis

PurposeThe purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis.Design/methodology/approachIn this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship.FindingsThe experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship.Research limitations/implicationsDue to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data.Practical implicationsThe focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining.Originality/valueThis study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.