This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, ...including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems.
The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications.
The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications.
This book has been adopted as a textbook at the following universities:
University of Stuttgart, GermanyRoyal Institute of Technology, SwedenJohannes Kepler University, AustriaGeorgia Tech, USAUniversity of Washington, USAOhio University, USA
Diese Open-Access-Publikation ist ein Plädoyer, das Verständnis von Themenkarrieren als integralen Bestandteil der Wissenschaft zu vertiefen und für die Reflexion wissenschaftlicher und planerischer ...Praxis zu nutzen. Welchen Gegenständen die Wissenschaft besondere Aufmerksamkeit beimisst, unterliegt einem dynamischen Wandel. Einige Themen, die lange Zeit Desinteresse und Ablehnung hervorriefen, rücken schlagartig in den Fokus um anschließend wieder abnehmende Aufmerksamkeit zu verzeichnen. Weder die Ursache noch der Zeitraum der anfänglichen Ignoranz, des abrupten Durchbruchs oder der anschließenden Ermüdung kann jedoch aus „rein wissenschaftlichen“ bzw. vermeintlich „objektiven“ Selektionskriterien erklärt werden. Wie also entstehen Themen in der Wissenschaft? Anhand der Themenkarrieren Schrumpfende Städte und Klimawandel wird untersucht, wie Aufmerksamkeit für ein Thema entsteht, welche sozialen Mechanismen dem Themenverlauf zugrunde liegen und welche Auswirkungen Themenkarrieren auf die planungswissenschaftliche Disziplin haben. Hierbei werden quantitative Methoden der Bibliometrie und der Netzwerkanalyse mit qualitativen Methoden der empirischen Sozialforschung in einer institutionalistischen Perspektive vereint.
Abstract
Stock network is a type of financial network based on stock price data used for analysing stock market dynamics. In this paper, a directed stock network is developed. This model was built ...using 480 shariah-compliant stocks traded in Bursa Malaysia from the year 2016 until 2018. Transfer Entropy was used as a measuring tool to build the stock network. Different networks are built and evaluated using network analysis methods. To determine the important stocks in the networks, centrality measures are applied such as degree centrality. The findings showed that Borneo Oil Berhad (BRNL) is the most influential and important stock among the 480 shariah-compliant stock in the Bursa Malaysia.
In this paper, we propose a novel approach to the analysis of collaborative learning. The approach posits that different dimensions of collaborative learning emerging from social ties and content ...analysis of discourse can be modeled as networks. As such, the combination of social network analysis (SNA) and epistemic network analysis (ENA) analysis can detect information about a learner's enactment of what the literature on collaborative learning has described as a role: an ensemble of cognitive and social dimensions that is marked by interacting with the appropriate people about appropriate content. The proposed approach is named social epistemic network signature (SENS) and is defined as a combination of these two complementary network analytic techniques. The proposed SENS approach is examined on data produced in collaborative activities performed in a massive open online course (MOOC) delivered via a major MOOC platform. The results of a study conducted on a data set collected in a MOOC suggest SNA and ENA produce complementary results which can i) explain collaboration processes that shaped the creation of social ties and that were associated with different network roles; ii) describe differences between low and high performing groups of learners; and iii) show how combined properties derived from SNA and ENA predict academic performance.
•A network analytics approach for collaborative learning is proposed.•The approach combines social network analysis and epistemic network analysis.•The approach is validated with a dataset from a massive open online course.•Prediction of the structure of social ties and network roles with discourse is shown.•Prediction of performance (groups) with discourse is demonstrated.
•Explores connections and patterns created by the aggregated interactions in Facebook pages during disaster responses.•Analyzes social media data from the Facebook page of city of Baton Rouge during ...the 2016 Louisiana flood (Aug 12–Dec 1, 2016).•Analyzes social roles and key players using social network analysis.•Study recommends actions to improve the effectiveness of information diffusion via social media.
Social media, such as Twitter and Facebook, plays a critical role in disaster management by propagating emergency information to a disaster-affected community. It ranks as the fourth most popular source for accessing emergency information. Many studies have explored social media data to understand the networks and extract critical information to develop a pre- and post-disaster mitigation plan.
The 2016 flood in Louisiana damaged more than 60,000 homes and was the worst U.S. disaster after Hurricane Sandy in 2012. Parishes in Louisiana actively used their social media to share information with the disaster-affected community − e.g., flood inundation map, locations of emergency shelters, medical services, and debris removal operation. This study applies social network analysis to convert emergency social network data into knowledge. We explore patterns created by the aggregated interactions of online users on Facebook during disaster responses. It provides insights to understand the critical role of social media use for emergency information propagation. The study results show social networks consist of three entities: individuals, emergency agencies, and organizations. The core of a social network consists of numerous individuals. They are actively engaged to share information, communicate with the city of Baton Rouge, and update information. Emergency agencies and organizations are on the periphery of the social network, connecting a community with other communities. The results of this study will help emergency agencies develop their social media operation strategies for a disaster mitigation plan.
This study seeks to analyze the trends in research studies in the past decade which have utilized Google Trends, a new source of big data, to examine how the scope of research has expanded. Our ...purpose is to conduct a comprehensive and objective research into how the public use of Big Data from web searches has affected research, and furthermore, to discuss the implications of Google Trends in terms of Big Data utilization and application. To this end, we conducted a network analysis on 657 research papers that used Google Trends. We also identified the important nodes of the networks and reviewed the research directions of representative papers. The study reveals that Google Trends is used to analyze various variables in a wide range of areas, including IT, communications, medicine, health, business and economics. In addition, this study shows that research using Google Trends has increased dramatically in the last decade, and in the process, the focus of research has shifted to forecasting changes, whereas in the past the focus had been on merely describing and diagnosing research trends, such as surveillance and monitoring. This study also demonstrates that in recent years, there has been an expansion in analysis in linkage with other social Big Data sources, as researchers attempt to overcome the limitations of using only search information. Our study will provide various insights for researchers who utilize Google Trends as well as researchers who rely on various other sources of Big Data in their efforts to compare research trends and identify new areas for research.
•Google Trends already been used to analyse user's interests across various fields.•The purpose of Big Data utilization is now shifting toward forecasting from monitoring.•For accurate forecasting, additional analyses such as sentiment will be required.•Diverse sources (ex, SNS) are being analysed together to overcome various limitations•To conduct precise analysis using Big Data, access to raw data should be expanded
Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social ...networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function. This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.
Instructors' discussion design and facilitation have critical influences on online learning community development. Emerging network analysis methods were used to examine the development of an online ...learning community within a graduate-level course, the variations of an experienced instructor's discussion design, and the dynamics of her discussion facilitation. Results indicated that students gradually formed an interactive online learning community. The instructor, overall, played a facilitator role in this community; yet her participatory roles varied within different discussions during different time frames. Her participatory role evolved from a guide in the first class discussion, to varying roles, i.e., a facilitator, an observer, and a collaborator within different group discussions at the middle stages of the course, and to an observer in the course's later stages. Methodological implications for using social network analysis in online learning community research, and practical implications for designing and facilitating discussions that foster online learning communities were proposed.
•Emerging social network analysis methods were used to examine the development process of an online learning community.•Students formed an interactive and cohesive online learning community over time in discussions.•The instructor's participatory roles varied in class and group discussions at different time frames during the course.•Methodological implications for using social network analysis methods in online learning community research were proposed.•Practical implications for designing and facilitating discussions that can foster online learning communities were proposed.
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