150 The influence of social networks on knowledge transfer within and between healthcare organizations: a scoping review

ObjectivesSocial network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social...

The seventh SNA-KDD workshop is proposed as the seventh in a successful series of workshops on social network mining and analysis co-held with KDD, soliciting experimental and theoretical work on social network mining and analysis in both online and offline social network systems. In recent years, social network research has advanced significantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale offline social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are increasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topological properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining social networks. These issues have important implications on community discovery, anomaly detection, trend prediction and can enhance applications in multiple domains such as information retrieval, recommendation systems, security and so on. The past SNA-KDD workshops have achieved significant attentions from the world-wide researchers working in different aspects of social network analysis, including knowledge discovery and data mining in social network, social network modeling, multi-agent based social network simulation, complex generic network analysis and other related studies that can bring inspirations or be directly applied to social network analysis. Each year we received more than 30 submissions. The average acceptance rate is around 1/3.

Chapter 3 - Privacy-Preserving Social Network Integration, Analysis, and Mining

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

Candidate change agent identification among men at risk for HIV infection

Chapter 3 - Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections

Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach

Through an exhaustive review of the literature, this article looks at the applicability of social network analysis (SNA) in the field of humanresource development. The literature review revealed that a number of disciplines have adopted this unique methodology, which has assisted in the development of theory. SNA is a methodology for examining the structure among actors, groups, and organizations and aides in explaining variations in beliefs, behaviors, and outcomes. The article is divided into three main sections: social network theory and analysis, the social network approach and application to HRD. First, the article provides an overview of social network theory and SNA. Second, the process for conducting an SNA is described and third, the application of SNA to the field of HRD is presented. It is proposed that SNA can improve the empirical rigor of HRD theory building in such areas as organizational development, organizational learning, leadership development, organizational change, and training and development.

Social network is defined as the interconnection of number of social entities with a variety of relationships. Usually social entities in a social network are of similar types. However, they may be heterogeneous. They can be interdependent through various relationships like financial transaction, message exchange, friendship, common interest, sexual relationships, common research ideas etc. The social network probably is the largest source of data deluge in the world of big data. Analysing large scale data and extracting useful information in the social network is one of the most challenging task of analysts. Social network analysis may involve content or structural analysis of the network. Social media data is increasing at an exponential rate. Traditional processing systems like relational database system, SQL, centralized processing units are unable to analyse such enormous amount of data. Distributed platform, where data can be distributed across multiple computing nodes can be adopted in analysing and extracting the useful information from the network. In this chapter, a different aspect of social network analysis along with their applications have been presented. This chapter focuses on structural analysis of social network rather than content analysis. It also discusses the impact of big data on the social network. Hadoop and Spark are found to be most suitable frameworks for big data analysis in social network. Hadoop is basically used for batch processing in cluster of nodes whereas Spark is suitable for streaming processing for real time data. Comparative analysis Hadoop and Spark performances have also been presented in this chapter.

Social network analysis dates back to the early 20th century, with initial studies focusing on small group behavior from a sociological perspective. The emergence of the Internet and subsequent increase in the use of online social networking applications has caused a shift in the approach to this field. Faced with complex, large datasets, researchers need new methods and tools for collecting, processing, and mining social network data.Social Network Mining, Analysis and Research Trends: Techniques and Applications covers current research trends in the area of social networks analysis and mining. Containing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science, this book proposes new measures, methods, and techniques in social networks analysis and also presents applications and case studies in this changing field.

As an emerging research approach, social network theory and analysis has been embraced and effectively applied in disciplines that have overlapping interests with sport management researchers including such fields as organizational behavior and sport sociology. Although a number of sport management scholars have investigated network-related concepts, to date no sport management studies have fully utilized the analytical tools that social network theory and analysis have to offer. In conjunction with a discussion about the ontological, epistemological, and methodological perspectives associated with network analysis, this article uses several examples from the sport management and organizational behavior bodies of literature to illustrate a number of the advantageous techniques and insights social network theory and analysis can offer. These examples are meant to provide a general understanding of the utility and applicability of the social network theory and analysis and potentially inspire sport management researchers to adopt a social network lens in their future research endeavors.

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>

A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of pattern of relationships among people, organizations, states and such social entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. Web can also be considered as a social network. Social networks are formed between Web pages by hyperlinking to other Web pages. In this paper a state of the art survey of the works done on social network analysis ranging from pure mathematical analyses in graphs to analysing the social networks in semantic Web is given. The main goal is to provide a road map for researchers working on different aspects of social network analysis