The Evolution and Application of Network Analysis Methods
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Social network is a reflection of a social structure, composed of actors and one or several kinds of their mutual ties. Social Network Analysis (SNA), matured in 1970s, is a piece of art and technology quantitatively analyzing social relations. Dynamic Network Analysis (DNA), proposed in 2002, integrates Social Network Analysis, Link Prediction, Multi-Agent Simulation methods and technology, and its core is the information collection, data processing and relationship forecasts and so on. Super network, proposed in the same year, refers to the above and beyond existing network, which has characteristics as multi-layer, multi-level, multi-dimensional, multi-attribute, congestion, and coordination. We have the techniques, tools and indicators of the three network analysis methods collated and summarized, and applied in opinion leader identification. The results proved that to identify and judge opinion leaders, especially the negative ones, with social network analysis, dynamic network analysis and super network analysis played a certain role in controlling network development, from the network behavior.Keywords:
Organizational network analysis
Social Network Analysis
Social network (sociolinguistics)
Network Analysis
Dynamic network analysis
Identification
Core network
Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this paper, we propose a data structure, called Community Tree, to represent the organizational structure in the social network. We combine the PageRank algorithm and random walks on graph to derive the community tree from the social network. In the real world, a social network is constantly changing. Hence, the organizational structure in the social network is also constantly changing. In order to present the organizational structure in a dynamic social network, we propose a tree learning algorithm to derive an evolving community tree. The evolving community tree enables a smooth transition between the two community trees and well represents the evolution of organizational structure in the dynamic social network. Experiments conducted on real data show our methods are effective at discovering the organizational structure and representing the evolution of organizational structure in a dynamic social network.
Organizational network analysis
Social network (sociolinguistics)
Dynamic network analysis
PageRank
Organizational structure
Tree (set theory)
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The present paper aims at developing a new research approach based on the synergy concept as a driving force in network analysis and modelling. Starting from some introductory reflections identifying the role of network synergy in regional development and transportation science, the paper aims at giving a new interpretation of synergy effects in a network by focussing the attention on three dimensions of economic network analysis, based on the related(network) production functions. These dimensions or levels are: network links, uni-modal networks and multi-modal networks. In the paper, first a static economic analysis is carried out with particular attention to the role played by connectivity and diversity among actors/segments/layers in a network. Next, the restructuring effects of either complementarity or competition between different links and modalities will be investigated by looking at the dynamic aspects of network performance by revisiting and investigating concepts from evolutionary ecology in connection with resilience and sustainability issues.
Complementarity (molecular biology)
Organizational network analysis
Dynamic network analysis
Network Analysis
Network Formation
Restructuring
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Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. Social network theory and methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to studying such dynamic network formation processes to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge regards link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this
Dynamic network analysis
Organizational network analysis
Network Formation
Social Network Analysis
Social network (sociolinguistics)
Evolving networks
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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. …
Network Analysis
Organizational network analysis
Social Network Analysis
Network theory
Dynamic network analysis
Social network (sociolinguistics)
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The present paper aims at developing a new research approach based on the synergy concept as a driving force in network analysis and modelling. Starting from some introductory reflections identifying the role of network synergy in regional development and transportation science, the paper aims at giving a new interpretation of synergy effects in a network by focussing the attention on three dimensions of economic network analysis, based on the related(network) production functions. These dimensions or levels are: network links, uni-modal networks and multi-modal networks. In the paper, first a static economic analysis is carried out with particular attention to the role played by connectivity and diversity among actors/segments/layers in a network. Next, the restructuring effects of either complementarity or competition between different links and modalities will be investigated by looking at the dynamic aspects of network performance by revisiting and investigating concepts from evolutionary ecology in connection with resilience and sustainability issues.
Complementarity (molecular biology)
Dynamic network analysis
Organizational network analysis
Network Analysis
Network Formation
Restructuring
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Degree distribution
Network model
Realization (probability)
Organizational network analysis
Hierarchical network model
Scale-free network
Network Formation
Network Analysis
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Currently,all kinds of complex network lie in the natural,physical,and social worlds,But we have little knowledge about hid common law of network. In order to make good use of network,we need to know more scientific things about network,named network science.The paper first analyses genesis,status, basic notion of network science and its influence on whole society,then discusses researchful hierarchical model of network science,including network structure layer for basic infrastructure,network modeling layer for formalization,network dynamic analysis layer for theoretical analysis and network function layer corresponding to different application,finally we give an example of network science model.
Organizational network analysis
Network Formation
Dynamic network analysis
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Network Analysis
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Research on complex networks has tightly relation with social network analysis.In fact,the fast growth of network science in the past decade is fertilized by the basic concepts and methods established by social network analysis.Meanwhile,social network analysis is also helped by the network science.This paper has briefly reviewed some of recent progress in the related topics done by our groups.The topics include cooperation-competition networks,the definition,detecting methods and index of significance of community structures,spatial structure of social networks and their consequence on the structure and dynamics of networks.
Social Network Analysis
Organizational network analysis
Social network (sociolinguistics)
Network Analysis
Dynamic network analysis
Network theory
Network Structure
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Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small‐world network model, and scale‐free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks.
Organizational network analysis
Social network (sociolinguistics)
Dynamic network analysis
Network Formation
Hierarchical network model
Social Network Analysis
Network model
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This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.
Organizational network analysis
Dynamic network analysis
Social Network Analysis
Resilience
Network Analysis
Social network (sociolinguistics)
Network Formation
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Citations (34)