"A further measure of global cohesion is the centralisation of a network. Where centrality relates to the position of particular points, centralisation relates to the overall structure of a network. Centralisation measures the extent to which the cohesion of a network is organised around a specific point or set of connected points. The spokes on a bicycle wheel, for example, form a highly centralised network around its hub. Measures of centralisation can be based on the degree, distance, or betweenness of points, and extensions of these concepts have involved the idea that it is possible to identify the sets of points that comprise the centre, margin, and periphery of the network as a whole." (John Scott," What is Social Network Analysis?", 2012)
"A whole collection of global measures cluster around the idea of the centrality of points within their graphs. The degree of a point - its total of incoming and outgoing lines—is the most basic measure and has been termed local centrality. Calculating the degrees of all points in a network and ranking them from highest to lowest gives a rank order of local centrality. This centrality is ‘local’ because it highlights points that are well-connected in their immediate neighbourhoods. Such points may not, however, be central in the more global sense that a circle or sphere has a unique centre that can be understood in quasi-spatial terms. Locally central points are well-connected within particular parts of the network but may not be at all well-connected in a global sense." (John Scott," What is Social Network Analysis?", 2012)
"The converse of centrality is peripherality and it can be useful to know those points that are least close to other members of their networks. Such points are not isolated but are poorly integrated into their network. They are likely to have little influ-ence and to be uninvolved in significant communication flows through the network." (John Scott," What is Social Network Analysis?", 2012)
"Betweenness Centrality makes the assumption that all communication between nodes happens along the shortest path and with the same frequency, which isn’t always the case in real life. Therefore, it doesn’t give us a perfect view of the most influential nodes in a graph, but rather a good representation." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
"Centrality algorithms are used to understand the roles of particular nodes in a graph and their impact on that network. They’re useful because they identify the most important nodes and help us understand group dynamics such as credibility, accessibility, the speed at which things spread, and bridges between groups." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
"Keep in mind that centrality measures represent the importance of a node in comparison to other nodes. Centrality is a ranking of the potential impact of nodes, not a measure of actual impact. For example, you might identify the two people with the highest centrality in a network, but perhaps policies or cultural norms are in play that actually shift influence to others. Quantifying actual impact is an active research area to develop additional influence metrics." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
"Sometimes the most important cog in the system is not the one with the most overt power or the highest status. Sometimes it’s the middlemen that connect groups or the brokers who the most control over resources or the flow of information. Betweenness Centrality is a way of detecting the amount of influence a node has over the flow of information or resources in a graph. It is typically used to find nodes that serve as a bridge from one part of a graph to another." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
"Use Degree Centrality if you’re attempting to analyze influence by looking at the number of incoming and outgoing relationships, or find the 'popularity' of individual nodes. It works well when you’re concerned with immediate connectedness or near-term probabilities. However, Degree Centrality is also applied to global analysis when you want to evaluate the minimum degree, maximum degree, mean degree, and standard deviation across the entire graph." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)
"The KG serves as the central reference for all structured and unstructured data related to a domain. Because a KG represents information by focusing on the meaning of data, users can overcome challenges related to data types, formats, and provenance, connecting information from multiple data sources. [...] A KG represents the core information and big picture of a domain. Humans can analyze, visualize, and query graph data to extract insights. Inference rules and machine learning algorithms are performed on top of the KG to infer new information not explicitly encoded within the KG. Analysts can use methods such as centrality and connectivity analysis to identify influential nodes, network analysis to detect the shortest path between nodes, and community analysis to recognize groups of similar nodes." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)
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