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Social Network Analysis
Published in Michael Muhlmeyer, Shaurya Agarwal, Information Spread in a Social Media Age, 2021
Michael Muhlmeyer, Shaurya Agarwal
In simplest terms, centrality describes how connected a node is to the network [23]. A centralized node will be highly connected to several other important nodes and hence have easier access to a number of network members when compared to a low centrality node. As there are many ways to define the importance of a node based on its connectivity, there are multiple methods used to define centrality quantitatively. Popular centrality measurements including degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and Katz centrality, to name a few. In Figure 5.4, nodes of high centrality are readily apparently by their high level of connectivity and importance to the network structure. The removal of these nodes would considerably change the network structure, while outlier nodes with few connections would keep the basic structure of the network intact.
Structure of networks
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
Closeness centrality [6] tries to quantify the centrality of a node, based on its proximity to all other nodes in the network. The mean geodesic distance from a given node to all other nodes in a network is given as Li=1n−1∑j(≠i)dij
Routing in Delay and Disruption Tolerant Networks
Published in Aloizio Pereira da Silva, Scott Burleigh, Katia Obraczka, Delay and Disruption Tolerant Networks, 2019
Aloizio P. Silva, Scott Burleigh, Katia Obraczka
Bubble Rap: taking into account that human interaction is dynamic in terms of hubs and communities, Bubble Rap combines the concept of community structure with node centrality to make routing decisions [144]. Centrality can be defined as a measurement of the structural importance to identify the key node to bridge a message in the network. There are two principal principles considered by Bubble Rap routing protocol: The roles and popularities that people have in society are replicated in networks. In this case, the Bubble Rap strategy first forwards messages to nodes which are more popular than the current node.People are always forming communities in their social lives. The same behavior it is observed in the network layers. In this case, the Bubble Rap looks for the members of destination nodes’ communities and uses them as relay nodes.
Skills Expectations in Cybersecurity: Semantic Network Analysis of Job Advertisements
Published in Journal of Computer Information Systems, 2023
While network creation is a machine learning process requiring minimum human intervention, interpreting the network created to extract its meanings depends on subjective evaluations. This is a crucial and creative step. The complete network map created in the first step often includes too many words and less prevalent edges that blur the most interesting substructure in the map. A good practice for interpreting an SN is to first identify several central words, which can form a substructure that conveys different meaningful themes and topics. In this regard, we characterize each word on basis of its centrality, a data metric in social network analysis.45 Centrality is a numerical property of a node that defines the relative importance of the node in a network, thus an indicator of influential nodes in the network. Among a variety of centrality measures, PageRank centrality46 is particularly appealing to our study in that the measure uncovers nodes whose influences extend beyond their adjacent words and into a broader network.47 Based on subject-matter knowledge the substructure can then be expanded by including additional words adjacent to the central words and other meaningful words linked to the adjacent words
Multisectoral action coalitions for road safety in Brazil: An organizational social network analysis in São Paulo and Fortaleza
Published in Traffic Injury Prevention, 2022
Adam D. Koon, Angelica Lopez-Hernandez, Connie Hoe, Andres I. Vecino-Ortiz, Flávio J. C. Cunto, Manoel M. de Castro-Neto, Abdulgafoor M. Bachani
For the network analysis, we used sociograms and social network measures to analyze the structure of relationships in both cities. We calculated centrality measures, weighting the connections by frequency of interaction. Centrality is a measure of a node’s overall influence in the network and is measured by (1) degree (a node’s number of connections), (2) closeness (a node’s distance to other nodes), and (3) betweenness (a node’s frequency of location in the connection between 2 other nodes). Finally, we calculated an eigenvector measure (a node’s connection to other well-connected nodes) and reach (portion of a network within 2 steps of an element) to understand key organizations for information flow (Valente 2010; Kumu 2020). Collectively, these measures locate the primary leaders within a network and the extent to which they engage and broker assets with others. All network visualizations were constructed using Kumu (2020), and statistical analyses were carried out using STATA version 15.
Connectivity evaluation of large road network by capacity-weighted eigenvector centrality analysis
Published in Transportmetrica A: Transport Science, 2021
Hiroe Ando, Michael Bell, Fumitaka Kurauchi, K. I. Wong, Kam-Fung Cheung
Topological methods focus on node connections in a network and do not consider geographic location. This is particularly so with centrality measures, which originate from social science analyses (Newman 2010). Centrality is a value that indicates which node is ‘central’ in the network, and there are various definitions of centrality. The idea of centrality was first applied in social networks to understand human community structure in small groups (Bavelas 1948). Subsequently, this concept has been adopted in various fields, including diffusion of infectious diseases, information and communication systems, economics and engineering. Table 1 summarises and references representative centrality measures with reference to Newman (2010); xi represents the centrality value of node i, N is the number of nodes, dij is the distance between node i and node j, is the number of shortest paths from s to t that traverse node i, gst is the total number of shortest paths from s to t, aij is an element of the adjacency matrix A, is the out-degree of node j, and α and β are positive constants.