How social networks influence the local implementation of initiatives developed in quality improvement collaboratives in health care
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Background Quality improvement collaboratives (QICs) have facilitated cross-organizational knowledge exchange in health care. However, the local implementation of many quality improvement (QI) initiatives continues to fail, signaling a need to better understand the contributing factors. Organizational context, particularly the role of social networks in facilitating or hindering implementation within organizations, remains a potentially critical yet underexplored area to addressing this gap. Purpose We took a dynamic process perspective to understand how QI project managers’ social networks influence the local implementation of QI initiatives developed through QICs. Methodology We explored the case of a QIC by triangulating data from an online survey, semistructured interviews, and archival documents from 10 organizations. We divided implementation into four stages and employed qualitative text analysis to examine the relationship between three characteristics of network structure (degree centrality, network density, and betweenness centrality) and the progress of each QI initiative. Results The progress of QI initiatives varied considerably among organizations. The transition between stages was influenced by all three network characteristics to varying degrees, depending on the stage. Project managers whose QI initiatives progressed to advanced stages of implementation had formed ad hoc clusters of colleagues passionate about the initiatives. Conclusion Implementing QI initiatives appears to be facilitated by the formation of clusters of supportive individuals within organizations; this formation requires high betweenness centrality and high network density. Practice Implications Flexibly modifying specific network characteristics depending on the stage of implementation may help project managers advance their QI initiatives, achieving more uniform results from QICs.Keywords:
Social Network Analysis
Social network (sociolinguistics)
The importance of an actor in the network is measured by the different type of centrality metrics of Social Network Analysis (SNA). In the research community, who are the most prominent author or key on the network is the major discussion or research issue. Different types of centrality measures and citation based indices are available, but their result is varied from network to network. In this paper, we form a network of author and its co-author based on Maximum Spanning Tree and find out the key author based on social network analysis metrics like degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. After that we compare the result of all centrality measures of MST based network and original network, betweenness centrality value increases and the other centrality value decreases. Finally, we conclude that the betweenness centrality is useful to analyze key author in this type of network.
Katz centrality
Social Network Analysis
Network theory
Network Analysis
Network controllability
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This paper introduces flowthrough centrality, a node centrality measure determined from the hierarchical maximum concurrent flow problem (HMCFP). Based upon the extent to which a node is acting as a hub within a network, this centrality measure is defined to be the fraction of the flow passing through the node to the total flow capacity of the node. Flowthrough centrality is compared to the commonly-used centralities of closeness centrality, betweenness centrality, and flow betweenness centrality, as well as to stable betweenness centrality to measure the stability (i.e., accuracy) of the centralities when knowledge of the network topology is incomplete or in transition. Perturbations do not alter the flowthrough centrality values of nodes that are based upon flow as much as they do other types of centrality values that are based upon geodesics. The flowthrough centrality measure overcomes the problem of overstating or understating the roles that significant actors play in social networks. The flowthrough centrality is canonical in that it is determined from a natural, realized flow universally applicable to all networks.
Katz centrality
Network controllability
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This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path centrality have high node betweenness centrality. Experimental evaluations on diverse real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared to known randomized algorithms.
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Background/Objectives: Secondary battery is expanding large secondary battery for various applications. In this study, joint research trends were analysed using network analysis in order to investigate the R&D of secondary batteries. Methods/Statistical Analysis: Degree centrality and betweenness centrality among the network analysis methods, the complex degree centrality was used to analyse the joint research network. Furthermore, the relationships among the degree centrality, betweenness centrality, and complex degree centrality were analysed. The analysis results show that 78 out of 91 countries carried out joint research, with the exclusion of 13 countries that did not participate in international joint research. Results: The joint research between China and USA institutions was most active, and China actively conducted joint research with Asian countries. A cluster analysis of the countries participating in joint research found that there were seven clusters in total, and the USA, Germany, France, and the U.K. played central role in each cluster. A correlation analysis of the centrality indices analysis results by country showed strong positive (+) correlation among the three indices. Furthermore, a regression analysis showed that the greater the complex degree centrality was, the greater the degree centrality and betweenness centrality became, and the increasing rate of the betweenness centrality was very high. Conclusion/Application: This study is meaningful that the correlations between complex degree centrality and other centrality indices were analysed in network analysis and investigated joint research status. Keywords: Centrality Indices, Network Analysis, PFNet, Regression, Secondary Battery
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To solve the problem that how to find important nodes accurately and quickly in complex and huge AS-level networks,a valuable research of centralization was carried out.With three common measurement such as degree centrality,closeness centrality,betweenness centrality,core centrality was proposed to act as an indicator to measure high-core nodes combination.The importance of a node within a network was characterized as the extent of which the network has been destroyed by deleting the node.The applicability of the closeness centrality was not good as degree centrality and betweenness centrality for Internet AS-level.There was high similarity in degree centrality and betweenness centrality when the proportion of attack nodes was smaller than 0.5%.Core centrality was applied to finding out the communities which were made up of high-degree and closely-connected nodes within networks.
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Katz centrality
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In recent years, social network theory becomes more and more significant in social science. Basing on the fast-growing social network theory, SNA (Social Network Analysis) is also widely used and published in different journals. As social actors are like nodes in the network, we use centrality to measure these nodes in power, activity and communication convenience etc.. Degree centrality, betweenness centrality and closeness centrality are main detailed measurement, and they have different algorithm. In SNA study, the research purpose determines the selection of centrality; and the use of these three centralities constitutes an important part in SNA study.
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Network theory
Closeness
Katz centrality
Social network (sociolinguistics)
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One of the most recent studies on the analysis of complex systems is to understand the role of community structure and centrality in analyzing the networks of complex systems such as protein and social networks. Traditional measures of centrality – degree centrality, closeness centrality, and betweenness centrality – cannot capture how community structures within these networks configure them. In this regard, we propose a new community-consideration centrality method to fill this gap. This method includes a weight of consideration, α, ranging from 0.0 to 1.0, to balance the focus between community and network-wide importance in the centrality calculations. Our analysis of two zachary karate and dolphin datasets shows that including community consideration in the degree, closeness, and betweenness centrality measures accurately captures the proportional significance of both communities and networks. In particular, for the lung adenocarcinoma cancer protein case study, our method not only identified more cancer hallmark genes than the traditional centrality measures without considering communities but also outperformed several other advanced centrality algorithms regarding the detection of crucial cancer-related genes. A balanced objective between network and community impacts was observed at an optimum performance α values of 0.1 and 0.2. It finds a strong significance of community structure in network analysis and features a more nuanced perspective on centrality in complex systems.
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The centrality of a node is a crucial indicator to understand how important this node is in a network, and several measures of centrality have been used to realize the identification. In this paper, we propose a new one named load centrality based on the betweenness changes caused by the removal of some nodes in the network. This measure of centrality combines the ideas: more important node is closer to others and more important node stands between more node pairs. By comparing with the other point centrality measures, the load centrality is validated further.
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Importance of estimating the centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications such as social network analysis or route planning. In this paper we find a formula to obtain the betweeness-centrality for grids.
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In this paper we study deliberate attacks on the infrastructure of large scale-free networks. These attacks are based on the importance of individual vertices in the network in order to be successful, and the concept of centrality (originating from social science) has already been utilized in their study with success. Some measures of centrality however, as betweenness, have disadvantages that do not facilitate the research in this area. We show that with the aid of scale-free network characteristics such as the clustering coefficient we can get results that balance the current centrality measures, but also gain insight into the workings of these networks.
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