China
Africa
Explore chapters and articles related to this topic
Actor networks in urban water governance
Published in Thomas Bolognesi, Francisco Silva Pinto, Megan Farrelly, Routledge Handbook of Urban Water Governance, 2023
Manuel Fischer, Karin Ingold, Mert Duygan, Liliane Manny, Katrin Pakizer
Networks can be analyzed on three different levels, the micro-level of individual actors in the network, the meso-level substructures within the network, that is, sets of nodes and ties in the network, or the macro-level of the entire network (Wasserman and Faust 1994; Borgatti et al. 2009; Borgatti, Everett, and Johnson 2018). The most common, important, and straightforward measure related to networks at the micro-level of network nodes is centrality. Most generally, centrality describes how central an actor is within the network, that is, the relational position of a given node in the overall network. Centrality, however, can take different forms (Freeman 1978). First, degree centrality is based on the simple number of incoming ties (in-degree) and outgoing ties (out-degree) of a node. Second, betweenness centrality takes into account the degree to which a node is located on the shortest path between any two other nodes in the network. Network nodes with high betweenness centralities are potentially important bridging actors or brokers in the network. Third, closeness centrality identifies the actors with the shortest paths (sequence of ties) to all other actors in the network, on average. Also in urban water governance studies, researchers are interested in more or less central actors, who might not only dominate the management processes, but also facilitate communication and finally the provision of (sanitation) services (Walters 2016).
Networks: The Basics
Published in Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, Big Data and Social Science, 2020
Consider Figure 9.6, which presents visualizations of the main component of two university networks. Both of these representations are drawn from a single year (2012) of UMETRICS data. Nodes represent people, and ties reflect the fact that those individuals were paid with the same federal grant in the same year. The images are scaled so that the physical location of any node is a function of its position in the overall pattern of relationships in the network. The size and color of nodes represent their betweenness centrality. Larger darker nodes are better positioned to play the role of brokers in the network. A complete review of the many approaches to network visualization and their dangers in the absence of descriptive statistics, such as those presented here, is beyond the scope of this chapter, but consider the guidelines presented in Chapter 6 on information visualization as well as useful discussions by Powell et al. (2005) and Healy and Moody (2014).
Correlation study of safety and emergency management information systems based on social network analysis
Published in Chongfu Huang, Zoe Nivolianitou, Risk Analysis Based on Data and Crisis Response Beyond Knowledge, 2019
Yi Zhou, Xuewei Ji, Aizhi Wu, Fucai Yu, Yonghua Han, Liping Fang, Yanyan Zhang
Betweenness centrality refers to the interval degree between one node and other nodes in the network, describing its value as a mediator. A bigger betweenness centrality means that more nodes need to pass through this node on the way of contacting other individuals. If one node is on the shortest path of connecting the other two nodes, it has a big betweenness centrality. If the betweenness centrality is 0, it means that the node cannot control any node and is at the edge of the network. According to the calculation results of Ucinet software, the top two nodes are the number of hidden hazards and the inspection times for full-time safety supervisors. The relative betweenness centrality values are 0.752 and 0.627, respectively. It shows that there are more short paths passing through these two nodes, and they can largely control the data flow of the whole network.
TriBeC: identifying influential users on social networks with upstream and downstream network centrality
Published in International Journal of General Systems, 2023
In the related literature, we found numerous studies for deriving the centrality of nodes in social networking sites. Well-known methods for identifying the importance of a node tend to make use of structural information as it is fundamentally affected by the topological structure of a network (Guilbeault and Centola 2021). Centrality was first defined in 1948 for the study of communication networks (Bavelas 1948, 1950). Later, three major types of centrality measures were developed, termed as degree, betweenness and closeness (Freeman 1978). The influence of a node often depends upon its direct neighborhood, i.e. local information in case of degree centrality. However, it lacks in considering the global structure of the network. Closeness centrality was introduced to incorporate indirect links and the value is calculated using geodesic distance to every other node in a network. Betweenness centrality considers the global information of a network which calculates the strength of a node in controlling the flow of information through the whole network. Several other measures including Eigenvector, Pagerank, Harmonic, Katz, Cross clique, Eccentricity, and percolation centrality capture the variations on the notion of node importance in a network (Bonacich 1972, 1987, 2007; Brin and Page 1998; Faghani and Nguyen 2013; Hage and Harary 1995; Katz 1953; Marchiori and Latora 2000; Piraveenan, Prokopenko, and Hossain 2013).
An overview of maritime logistics: trends and research agenda
Published in Maritime Policy & Management, 2023
Seçil Gülmez, Gül Denktaş Şakar, Sedat Baştuğ
The betweenness centrality ranges from 0 to 7.980 in this network. According to betweenness centrality values, Cluster 5 has the highest betweenness centrality value because of incorporating Islam (), Panayides and Song) and Nam and Song) publications with the 6.011, 5.930, and 5.153 values, respectively. The second and third-highest betweenness centrality values are of Cluster 1 and Cluster 2. Finally, Cluster 7 has the lowest value. As the highest betweenness and centrality means being an information hub in the network, hinterland logistics can be considered to have more control over the network. This could be due the reason that information passes through this node. In this case, hinterland logistics (Cluster 5) has a central role in the maritime logistics field as well as port operations (Cluster 1) and ship operations (Cluster 2).
Investigating network structure of cross-regional environmental spillover effects and driving factors
Published in Journal of the Air & Waste Management Association, 2020
This paper will describe the network characteristics from both the overall and the individual aspects. Network density and correlation degree are selected to represent the overall characteristics. The former denotes the degree of tightness between nodes, and the greater the value, the stronger the connection between nodes. The latter measures the robustness of the network, and the closer the value is to 1, the stronger the robustness of the network is. Degree centrality, closeness centrality, and betweenness centrality are selected to represent individual characteristics. Degree centrality denotes the number of edges connected with this node, and the larger the value, the more important the role of this node in the network. The closeness centrality measures the degree to which a node is not controlled by other nodes, the larger the value, the stronger the ability of this node to be uncontrolled by other nodes. Betweenness centrality measures the degree to which one node controls other nodes, and the larger the value, the better the node can control the relationship between other nodes.