To construct or to reveal? Network analysis as formalising communication

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...

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.

The innovation literature has a long-held tradition of using networks to understand processes of idea generation, opportunity recognition and the diffusion of knowledge. This dates back at least to Schumpeter (1912/1983), who talked about the importance of creating new combinations in the innovation process. However, the most dominant use of the network construct in the innovation research context to date is in its qualitative or metaphorical sense. For example, a study might interview a manager and ask them how important their professional network is for generating new ideas.While this has been a productive line of enquiry, new analytical techniques in graph theory (the quantitative analysis of networks) are only just starting to be applied to innovation research. When used to analyse social relationships, graph theory is generally referred to as network or social network analysis. The roots of this approach date back to the studies by Morello in psychology in the 1930s (Freeman, 2004).As network analysis has moved forward, sophisticated techniques in probabilistic network methods, weighted network and longitudinal network analysis have created further possibilities for understanding the interactions between network structures, agents and innovation across multiple levels of analysis. These techniques have been adopted from the physical sciences, and social network analysis has become complex network analysis (Newman, Barabasi and Watts, 2006). When the technical advances are combined with the recent increases in computing power, it has become much more feasible to use complex network analysis more broadly within the social sciences in general, and in innovation studies in particular.From this research we have begun to understand the importance of network structures and the relationship between agents and these structures in the process of innovation. Initial work in this area has focused on specifying the structure of business networks. For example, there have been several papers identifying networks with a 'small world' structure (short average distance through the network combined with high levels of clustering) (Verspagen and Duysters, 2004). More recent work has started to link structural characteristics of networks to innovation performance (Uzzi and Spiro, 2005; Schilling and Phelps, 2007).This special issue of Innovation: Management, Policy & Practice titled 'New Network Perspectives on the Innovation Process' (ISBN 978-1-921348- 32-7) looks at some of the state-of-the-art research incorporating complex network analysis in the study of the innovation process.The first paper by van der Valk and Gijbers (2010) provides an excellent overview of the use of social network analysis in innovation studies, reviewing all 49 papers using network analysis which have been published in the top 10 innovation journals. They then use social network analysis to identify the key issues that these techniques have been used to study: interpersonal and interorganisational collaboration networks, communication networks and technology and sectoral structures. Citation network analysis is one area of wide application for network analysis techniques. This paper provides a good overview of the use of social network analysis within innovation studies, which provides a useful context for the remaining papers in the special issue.The next paper by Maritz (2010) investigates the interactions between networks and entrepreneurial productivity in universities. He shows that academics with larger networks and with more frequent communication within these networks are both more entrepreneurial and more productive. This is an excellent example of the non-structural network papers. It makes extensive use of network concepts and ideas, and it demonstrates the importance of connections in generating novel ideas.Lee and Su (2010) use techniques that are similar to those of van der Valk and Gijsbers, but in this case their focus is on the research literature on regional innovation systems. …

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.

We present the software tool visone which combines graph-theoretic methods for the analysis of social networks with tailored means of visualization. Our main contribution is the design of novel graph-layout algorithms which accurately reflect computed analyses results in well-arranged drawings of the networks under consideration. Besides this, we give a detailed description of the design of the software tool and the provided analysis methods.

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

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.

Cultures of the Central Highlands, New Guinea

Scholarly work and research on communication in multinational organizations continues to grow, responding to the increase of organizational complexity in a global environment where international teams, initiatives, and joint ventures have become common. Accompanying that growth were efforts to establish a clear focus and define boundaries of organizational communication research, particularly emphasizing multinational organizations. How to define communication in the context of multinational organizations? While a comprehensive review of the answers to this question could yield a handbook of communication in organizations, a clear answer can be given outlining the assumptions and political interests underlying different perspectives and theoretical conceptualizations. Therefore, instead of answering the question of what communication is in multinational organizations, this article follows the question proposed by Stanley Deetz. In The New Handbook of Organizational Communication, edited by Fredric M. Jablin and Linda L. Putnam (Thousand Oaks, CA: SAGE, 2001), Deetz asks, “What do we see or what are we able to do if we think of organizational communication in one way versus another?” (p. 4). Deetz poses the question in order to better understand our choices of setting boundaries for the study of communication in organizations. Deetz reviews three different ways of conceptualizing communication in organizations. The first one emphasizes the development of organizational communication as a specialized area where departments and associations are organized around it; the second approach views communication as a phenomenon that exists in organizational context; and the third one regards communication as a distinct mode of explaining organizations. Recently there have been burgeoning studies in which communication scholars approach communication in organizations using the third approach. Those studies provide psychological or social-cultural explanations of organizations. This review summarizes several major topics on communication in multinational organizations that have been studied over the years. Rather than providing a comprehensive review of the field, the select perspectives and topics discussed here reflect major research foci and approaches associated with the study of communication in multinational organizations in the last few decades. This discussion also captures the recent shift from classic organizations to knowledge-intensive organizations in the context of 21st-century organizational life.

Article Some Recent Developments in Organizational Communication: Network Analysis – A Systemic Representation of Communication Relationships was published on January 1, 1977 in the journal Communications (volume 3, issue 2).

This study proposes a hybrid clustering approach to identify the positions and roles in a relational network by integrating multivariate and social network analysis. First, an adjacency matrix was constructed based on the graph theory to indicate the relation between the collected data. Next, network analysis was conducted and the statistics of network centrality as clustering variables were computed. After, this study reduced clustering variables using the principal component analysis. These selected principal components were then used as clustering variables for a two-step cluster analysis. Hierarchical cluster analysis was first made to determine the appropriate number of clusters and then K-means clustering was used for dividing actors into k proper positions. In addition, the multivariate analysis of variance was conducted to test the significance between those positions. After, a new adjacency matrix was built upon the rearrangement of k positions. The frequency within and between these positions was computed and the cut-off value was determined to distinguish the difference between these frequencies. Finally, each position was labeled based on its characteristics and the relationships within and between these positions. After the structured approach was established, the litigation-related network of smartphone makers was used as empirical evidence. The results showed that this structured approach can effectively distinguish the position and role of a company in a relational network.

This study aims to map the narratives, networks, and influential figures on social media Twitter after the arrest of suspected terrorists (Farid Okbah cs and Sunardi) by Detachment 88 anti-terror in Indonesia. We use the social network and content analysis methods in collecting and analyzing research data. The results of this study show that the anti-Islamic political group with government (opposition) is currently trying to make a narrative that the Indonesian government is Islamophobic, by utilizing the momentum of suspected terrorist arrests in Farid Okbah and Sunardi by Detachment 88 by shifting the issue of terrorist arrests from the JI group who are also members of MUI with the demand for dissolving the Government Institution Detachment 88 Anti-terror. The social network map shows that there were three groups engaged in the arrest of suspected terrorists Farid Okbah and Sunardi, 1) Muslim Group Cyber Army (MCA); 2) Pro-government buzzer group through Denny Siregar’s account; 3) The provocateurs group. The arrest of Farid Okbah and Sunardi turned into political issues, Islamic groups (MUI) vs. Government (Detachment). The arrest of Farid Okbah by Detachment 88 was dominated by #saminawaathonaibhrs and #Islamophobia, while the dominant narrative after the suspected arrest of Sunardi’s is #PKItangkapulama (PKI Arrest Ulama) and #dukungMUI (Support MUI), both of these things showed an effort to accompany the issue to the political narrative of the opposition group to weaken the government with the narrative that discriminated government of Islamic groups.
 Keywords: Islamophobia, social network analysis, narrative analysis, social media

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.

In this chapter we discuss the use of social and dynamic network analysis for researching networks and collaboration in the public sector. Network analysis is an analytical toolbox and method that is strongly linked with the study of networks and collaboration, and has recently increasingly been applied in public management research. The chapter discusses the relevance of the method for public management research and how social network analysis has been applied in the field as well as its potential for future research. Finally, the chapter lists the most important literature, software and other resources for conducting social network analysis.