Potential Of Social Network Analysis Research Articles

Applying Social Network Analysis to Model and Handle a Cross-Blockchain Ecosystem

In recent years, the huge growth in the number and variety of blockchains has prompted researchers to investigate the cross-blockchain scenario. In this setting, multiple blockchains coexist, and wallets can exchange data and money from one blockchain to another. The effective and efficient management of a cross-blockchain ecosystem is an open problem. This paper aims to address it by exploiting the potential of Social Network Analysis. This general objective is declined into a set of activities. First, a social network-based model is proposed to represent such a scenario. Then, a multi-dimensional and multi-view framework is presented, which uses such a model to handle a cross-blockchain scenario. Such a framework allows all the results found in the past research on Social Network Analysis to be applied to the cross-blockchain ecosystem. Afterwards, this framework is used to extract insights and knowledge patterns concerning the behavior of several categories of wallets in a cross-blockchain scenario. To verify the goodness of the proposed framework, it is applied on a real dataset derived from Multichain, in order to identify various user categories and their “modus operandi”. Finally, a new centrality measure is proposed, which identifies the most significant wallets in the ecosytem. This measure considers several viewpoints, each of which addresses a specific aspect that may make a wallet more or less central in the cross-blockchain scenario.

Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter.

Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.

Sosyal Ağların İşgücü Piyasası Eşitsizlikleri ve Okuldan İşe Geçişlere Etkisi

Countries invest in education systems in order to increase the quality of their human capital. In this context, it is seen that especially after the expansion of the higher education systems, countries try to increase higher education graduation rates in order to improve the quality of human resources in the labor market. The ultimate goal of these efforts is to facilitate the transitions from school-to-work, and to increase social welfare by meeting the human resources needs of the labor market. The facilitation of school-to-work transitions has a direct impact on youth unemployment. School-to-work transitions are influenced not only by the quality of education from primary to higher education but also by the dynamics of the labor market. Social network analysis can provide important insights into this dynamics, and in doing so reveal that there are indeed many factors that play a key role in determining who gets a job and why, including, first and foremost, social contacts. An analysis of job search channels reveals that partners, friends, and relatives are those social contacts that are most decisive for employment outcomes. Research reveals that employers use social-contact-based reference channels much more frequently than formal channels for recruitment. Thus, employers frequently use such reference channels in recruitment. It has also been shown that the use of social-contact channels reduces employers' costs of finding suitable employees and increases productivity since employees hired through these channels also stay longer in their firms. We here explore the full potential of social network analysis to better our understanding of school-to-work transitions, to reveal in no uncertain terms the importance of social contacts, and to show how these insights can be leveraged to level the labor market for all involved. An important take-home message is that the labor market dynamics is strongly affected by the Matthew effect, such that the inequalities and the gaps between opportunities only grow and widen as the underlying social networks evolve. It is therefore important to mitigate these effects well before school-to-work transitions come into play, namely during the education. In particular, we assert that minimizing the inequalities during education should effectively mitigate the uneven impact of social networks on school-to-work transitions.

Networked dynamics of knowledge integration in strategic spatial planning processes: a social network approach

ABSTRACT Planning processes guiding sustainable urban futures are typically complex, non-linear and value-laden, but their knowledge dynamics are still not adequately understood. This paper explores the potential of social network analysis (SNA) as a part of mixed-method approach for analysing the dynamic social context of knowledge integration in planning processes. The empirical study uses detailed longitudinal data of a four-year statutory planning process in the Nordic context, providing a methodological contribution for understanding knowledge integration in planning with visual–analytical methods and actor-relational criteria. Findings provide a new understanding of the actual social realities of planning practice and the further conceptualization of situated process dynamics.

Education for Sustainable Development and Climate Change Education: The Potential of Social Network Analysis Based on Twitter Data

Education is considered an essential tool for achieving sustainability-related goals. In this regard, education for sustainable development (ESD) and climate change education (CCE) have become prominent concepts. The central characteristics of both concepts influence the non-hierarchical network governance structure that has formed around them: (1) their international origin, (2) the conceptual ambiguity that surrounds them, and (3) the limited implementing power of international organizations who developed these concepts. Hence, networks are essential to ESD and CCE, however, only few studies have used social network analysis (SNA) techniques to analyze their governance structure. The aim of this article is to illustrate how to use SNA, based on Twitter data, as an approach to examine the governance structure that has developed around ESD and CCE. We conduct an illustrative SNA, using Twitter data during three global climate change summits (2015-2017) to examine CCE-specific debates and identify actors exerting the most influence. We find that international organizations and international treaty secretariats are most influential across all years of the analysis and, moreover, are represented most often. These findings show that using SNA based on Twitter data offers promising possibilities to better understand the governance structure and processes around both concepts.

Understanding the dissemination and adoption of innovations through social network analysis: geospatial solutions for disaster management in Nepal and Kenya

Vulnerable areas of the world, including many developing countries, are increasingly exposed to natural disasters. New technologies, such as geospatial technologies, could help them manage the risks of extreme events and cope with disaster aftershock. However, new technologies are often disseminated slowly among the relevant stakeholders. Assuming that knowledge exchange through stakeholder networks can effectively enhance the uptake of innovation, this research applied a social network approach focussing on the structural patterns of communication and collaboration networks regarding landslide-related disasters in Nepal and floods in Kenya. Using methods of formal social network analysis, we reveal centrally positioned stakeholders and discuss their actual and potential roles in outscaling innovations between the different sectors and upscaling them to different levels within the disaster management communities under study. In doing so, this case study demonstrates the potential of social network analysis for improving the dissemination of innovations for disaster risk management.

The dynamics of grooming interactions: maintenance of partner choice and the consequences of demographic variation for female mandrills.

A large body of evidence suggests that female Old World monkeys maintain selective long-term grooming interactions with fitness benefits. The last two decades have produced evidence that the regulation of social interactions among primates can be, in part, explained by the Biological Markets theory, with grooming behaviour as the focus of these studies. Grooming facilitates bonding between individuals, constituting an essential part of the regulation of social relationships among female cercopithecids. In contrast to the well-studied baboons (Papio spp), knowledge about the nature of grooming interactions and their regulation is generally lacking for the large, terrestrial species of mandrills (Mandrillus sphinx). We used a combination of social network analysis tools and well-established methods for assessing partner diversity and reciprocity to characterise grooming networks, partner choice and patterns of trade (be groomed, give grooming) among females in a captive group of mandrills, both within and across two separate observation periods. Our results suggest that, even though the relatively stable conditions of captivity allowed the studied females to maintain selective grooming interactions across time, small scale demographic changes affected the grooming dynamics of the group in accordance with the expectations of the Biological Markets theory. In particular, the maturation and consequent integration of a high ranking female into the group’s grooming network from one period to the next resulted in a more pronounced effect of rank on the regulation of grooming interactions. In addition, the influence of the maturation of a dependent infant on the grooming interactions of his mother were evident between periods. Our results also demonstrate that grooming networks are dynamic and that high ranking individuals are not necessarily the most central in grooming networks. Finally, we discuss the potential of social network analysis to identify cases of social exclusion and its consequences for captive management.

Social Network Analysis Facilitates Cognition in Large Wireless Networks: Clustering Coefficient Aided Geographical Routing

As large wireless networks for Internet of Things and cyber-physical systems emerge, they face challenges with new technology, scalability, and consequently effective cognition to execute networking functions. Beyond conventional sensing, researchers recently note the potential of social network analysis and inference to comprehend the networking status, but straightforwardly focus on the first-order statistics such as connectivity, degree, and centrality. Instead, we reveal a new alternative to equip network nodes with topological cognition by estimating the clustering coefficient. We further exploit such new cognitive capability to enable more efficient routing algorithms. Starting from random geometric graph to treat the communication range of a node as a disc, which is equivalent to examining the outage probability of links under stochastic geometry analysis of interference, the impacts of density and traffic of nodes on the connectivity can be analytically understood. Similar to the structural holes in social networks, the ad hoc networks suffer from the void region problem in the geographical routing, which is even more harmful in dense environments. Discarding the global routing table that demands non-scalable communication overhead, the clustering coefficient estimation of nodes as a new cognitive functionality can significantly alleviate the void problem to result in more effective ad hoc networking.

A scoping review on the current and potential use of social network analysis for evaluation purposes

The pervasiveness and importance of relationships and networks has fueled the development of the social network analysis approach, which considers structural relationships to be primary causes of societal outcomes. While the potential of social network analysis has been demonstrated and discussed extensively in social science research, relatively little is known about the current and potential use of social network analysis for evaluation purposes. This scoping review of journal articles reveals that evaluators use social network analysis because of its ability to identify key stakeholders, assess network structures and relationships quantitatively, reveal informal relations and visualize even complex networks. However, challenges arise when interpreting findings, determining causation between network structures and outcomes and disseminating evaluation results in an ethically responsible manner. The review concludes that the evaluation field―especially in the development sector―would benefit greatly from increased use of social network analysis, but that this would first require improved use of alternative sources of network data, qualitative methods and inferential statistics that will enable evaluators to move beyond descriptive network analysis.

Exploring the key issues and stakeholders associated with the application of rainwater systems within the Amazon Region

This paper presents a case study from the Amazon region of Brazil that explores stakeholder influence over the design and implementation of rainwater harvesting systems. This explorative study is based on the application of Social Network Analysis (SNA). A series of interviews were undertaken with experts in the field and the data was coded and analysed. A stakeholder's map is presented in an attempt to summarise the study’s main findings and to graphically illustrate the key stakeholder influences. The primary outcome of this exercise was the identification of the key participants and challenges associated with the implementation of rainwater harvesting systems. This exercise also highlighted the lack of interaction between some stakeholders and the canalization of decision-making powers by a small number of agencies. The scope of the study was limited to a specific geographical region and is therefore context specific. Due to the constraints of this preliminary study, the full potential of SNA has not fully been explored in this analysis. The research has identified some redundancies with regards to the management of water in this region. It has also highlighted other issues associated with ‘lack of inclusion’ within the decision-making process and planning for the implementation of rainwater systems. The study is considered to be novel within this geographical region. The use of such methods to map stakeholders and to graphically represent influential relationships, as well as the identification of previously unseen key actors should aid future attempts to implement rainwater harvesting schemes within this context.

Studying Discourse as Social Interaction: The Potential of Social Network Analysis for Discourse Studies

Education researchers have extensively studied classroom discourse as a way to understand classroom structures and learning. This article proposes the use of social network analysis (SNA) as a method for discourse studies in education. SNA enables us to learn about the connections between persons and the patterns of relations within groups. This presents a novel approach to the study of discourse that may more accurately reflect current understandings of discourse as a social phenomenon. This article explains the theoretical links between SNA and the concept of discourse in education and then considers how SNA can be used to examine classroom discourse. A brief overview of promising methods is presented to provide examples of how SNA can be applied to discourse data. This article argues that continued exploration and applications of SNA could yield more complex understandings of the role of discourse in learning opportunities and outcomes.

Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks

Research on group cohesion often relies on individual perceptions, which may not reflect the actual social structure of groups. This study draws on social network theory to examine the relationship between observable structural group characteristics and individual perceptions of group cohesion. Leveraging Facebook data, we extracted and partitioned the social networks of 109 participants into groups using a modularity algorithm. We then surveyed perceptions of cohesion, and computed group density and size using social network analysis. Out of six linear mixed effects models specified, a random intercept and fixed slope model with group size as a predictor of perceived group cohesion emerged as best fitting. Whereas group density was not linked to perceived cohesion, size had a small negative effect on perceived cohesion, suggesting that people perceive smaller groups as more cohesive. We discuss the potential of social network analysis, visualization tools, and Facebook data for advancing research on groups.

Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling

Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their ‘capacity to innovate’ and contribute to the ‘scaling of innovations’. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over- and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors) are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts.

Relating supply network structure to productive efficiency: A multi-stage empirical investigation

The potential of Social Network Analysis (SNA) to characterize supply network structure is of growing interest in supply chain management, although the related literature provides few empirical investigations. This study identifies those SNA measures most closely associated with supply chain efficiency, using archival inter-firm relationship data collected from U.S. public companies in multiple industries. In a three-stage procedure, a DEA model is applied to measure firm- and chain-level efficiencies, followed by a correlation analysis to group SNA variables into clusters of high correlation. These clusters are used in a step-wise regression algorithm to identify those variables most relevant to productive efficiency while accounting for multicollinearity. The supply network structural characteristics that emerge as significant are consistent with many hypothesized relationships in the literature, although not without exceptions, such as an interesting tradeoff between the benefit of connectedness and a penalty for closeness.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

Developmental Dynamics of Intergroup Contact and Intergroup Attitudes: Long-Term Effects in Adolescence and Early Adulthood.

Intergroup contact represents a powerful way to improve intergroup attitudes and to overcome prejudice and discrimination. However, long-term effects of intergroup contact that consider social network dynamics have rarely been studied at a young age. Study 1 validated an optimized social network approach to investigate intergroup contact (N=6,457; Mage =14.91years). Study 2 explored the developmental trajectories of intergroup contact by applying this validated network approach in a cross-sequential design (four-cohort-four-wave; N=3,815; 13-26years). Accelerated growth curve models showed that contact predicts the development of attitudes in adolescence, whereas acquired attitudes buffer against decreasing contact in adulthood. Findings highlight the potential of social network analysis and the developmental importance of early intergroup contact experiences.

Sensing Pollution on Online Social Networks: A Transportation Perspective

Transportation policy and planning strategies, as well as Intelligent Transportation Systems (ITS), can all play important roles in decreasing pollution levels and their negative effects. Interestingly, limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks provide direct feedback from people and, hence, potentially valuable information. A post telling how a person feels about pollution at a given time at a given location, could be useful to policy-makers, planners or environmentally-aware ITS designers. This work verifies the feasibility of sensing air pollution from social networks and of integrating such information with real sensors feeds, unveiling how people advertise such phenomenon, acting themselves as smart objects, and how online posts relate to true pollution levels. This work explores a new dimension in pollution sensing for the benefit of environmental and transportation research in future smart cities, confronting over 1,500,000 posts and pollution readings obtained from governmental on-the-field sensors over a one-year span.

The potential of Social Network Analysis as a tool for the management of zoo animals

AbstractSocial Network Analysis (SNA) enables the fine scale of animal sociality and population structure to be quantified. SNA is widely applied to questions relating to behavioural ecology but has seen little use in the application to zoo animal management, despite its clear potential. Investment in social bonds between individuals positively affects health status, welfare state, long-term fitness and lifetime reproductive output. Such positive affective states can be maintained consistently within captive situations if more information is known about the social preferences of the individuals that are kept. Disruption to social bonds may lead to impoverished welfare and stress to individuals which have seen their social support compromised. The patterning of social relationships between individuals also influences how space is utilised and how animals interact with resources provided for them. With more detailed knowledge of the social structure of a group or population, social groupings (for example, for captive breeding) can be specifically designed to minimise social stress. Likewise, enhancing the chances of successful reproduction can be achieved if we understand the role that each individual within a network plays and how these roles may impact on the behaviour of others. This paper discusses key aspects of SNA applicable to zoo-based researchers wishing to investigate the social lives of zoo animals. We present a review of how SNA can be used to assess social behaviour and highlight directions for future research. Our aim is to stimulate new research to ultimately improve our understanding of reproductive success, decision-making, group leadership, animal health and enclosure use. We conclude that what can be learned about the dynamics of social zoo-housed species using SNA can directly impact on husbandry decisions and help underpin excellent standards of animal welfare.

Social network analysis in the science of groups: Cross-sectional and longitudinal applications for studying intra- and intergroup behavior.

Social scientists increasingly recognize the potential of social network analysis, which enriches the explanation of human behavior by explicitly taking its social structure into account. In particular for the science of groups, social network analysis has reached a point of analytic refinement that makes it a valuable tool for investigating some of the central mechanisms that underlie intra- and intergroup behavior. The present article highlights the general relevance of this scientific approach and describes the background, generation, and application of cross-sectional as well as longitudinal network statistics that are of specific interest to group researchers. In doing so, we aim to provide a general introduction for researchers new to this approach, while demonstrating the potential and limitations of social network analysis for different areas in this field.

Music Genres as Historical Artifacts: The Case of Classical Music

This article reflects on the use of predetermined genre lists to measure patterns in music taste and, more specifically, classical music taste. Classical music as a whole is in quantitative research typically treated as marker of cultural prestige, although qualitative research suggests great internal diversity within the genre. The use of a predetermined array of genres to measure music taste risks to miss these subdivisions within the classical music genre and thus produces biased results. Therefore, inspired by Lamont’s (2010) call to study classification systems ‘from the ground up’, we present an alternative strategy to measure classical music taste using an open question about artist preferences. We build a two-mode network of classical music artists and respondents and use Infinite Relational Models to identify clusters of respondents that have similar relationships to the same set of artists. We detect no less than five distinct listening patterns within the classical music genre. Two of these preference clusters focus only on very central, popular classical artists. Another cluster combines these popular artists with more contemporary artists. One cluster focuses on only one very accessible artist and, finally, there is a cluster of respondents that distinct themselves by having a real connoisseur taste. Furthermore, we find that expert taste in classical music is not related to social distinction. Instead, knowledge of the most central and popular artists (e.g. Bach, Beethoven, Mozart) is typical for respondents with a high socio-economic background. Social distinction seems more related to knowledge of popular artists in classical music than to distinctive, connoisseur taste. Our findings show the potential of social network analysis for the problem of music taste classification and cultural sociology in general.