Abstract
Numerous centrality measures have been introduced as tools to determine the importance of nodes in complex networks, reflecting various network properties, including connectivity, survivability, and robustness. In this paper, we introduce Semi-Local Integration (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood. We illustrate SLI node importance differentiation among nodes in lexical networks and demonstrate its potential in natural language processing (NLP). In the NLP task of sense identification and sense structure analysis, the SLI centrality measure evaluates node integration and provides the necessary local resolution by differentiating the importance of nodes to a greater extent than standard centrality measures. This provides the relevant topological information about different subnetworks based on relatively local information, revealing the more complex sense structure. In addition, we show how the SLI measure can improve the results of sentiment analysis. The SLI measure has the potential to be used in various types of complex networks in different research areas.
Highlights
In the era of Big Data, enormous amounts of data are being collected and analyzed to gain important information and make decisions in a variety of application areas, from managing transportation networks, organizing distribution and delivery, studying biological networks, to organizing the Internet
We introduce Semi-Local Intregation (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood
We propose a new centrality measure SLI that evaluates the interconnectedness of nodes in a semi-local subgraph
Summary
In the era of Big Data, enormous amounts of data are being collected and analyzed to gain important information and make decisions in a variety of application areas, from managing transportation networks, organizing distribution and delivery, studying biological networks, to organizing the Internet. Graphs ecame the obvious choice for representing the information structure of many data systems. In addition to standard graph theory, a modern wave of mathematical approaches, techniques, and tools are being created, developed, and applied by scientists in various fields, in order to optimize such complex processes. One of the most important tasks in network analysis is the detection of central or important nodes, which is still a challenge as it depends on the context used. Many centrality measures are in common use, the category itself is not precisely defined. No formal theory has been developed to explain the differences in behavior among them. According to [1], ‘There is certainly no unanimity on exactly what centrality is or on its conceptual foundations, and there is little agreement on the proper procedure for its measurement’.
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