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Article

Semi-Local Integration Measure of Node Importance

1
Department of Mathematics, University of Rijeka, R. Matejčić 2, 51000 Rijeka, Croatia
2
Faculty of Humanities and Social Sciences, University of Rijeka, Sveučilišna Avenija 4, 51000 Rijeka, Croatia
*
Author to whom correspondence should be addressed.
Academic Editors: Jonatan Lerga, Ljubisa Stankovic, Nicoletta Saulig and Cornel Ioana
Mathematics 2022, 10(3), 405; https://doi.org/10.3390/math10030405
Received: 29 December 2021 / Revised: 17 January 2022 / Accepted: 25 January 2022 / Published: 27 January 2022
(This article belongs to the Special Issue New Trends in Graph and Complexity Based Data Analysis and Processing)
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. View Full-Text
Keywords: centrality measure; node importance; complex networks; applications of graph data processing; lexical graph analysis; sentiment analysis centrality measure; node importance; complex networks; applications of graph data processing; lexical graph analysis; sentiment analysis
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Figure 1

  • Externally hosted supplementary file 1
    Link: https://github.com/sbujacic/SLI-Node-Importance-Measure
    Description: This is the Python implementation of the Semi-Local Intregation (SLI) measure, 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.
MDPI and ACS Style

Ban Kirigin, T.; Bujačić Babić, S.; Perak, B. Semi-Local Integration Measure of Node Importance. Mathematics 2022, 10, 405. https://doi.org/10.3390/math10030405

AMA Style

Ban Kirigin T, Bujačić Babić S, Perak B. Semi-Local Integration Measure of Node Importance. Mathematics. 2022; 10(3):405. https://doi.org/10.3390/math10030405

Chicago/Turabian Style

Ban Kirigin, Tajana, Sanda Bujačić Babić, and Benedikt Perak. 2022. "Semi-Local Integration Measure of Node Importance" Mathematics 10, no. 3: 405. https://doi.org/10.3390/math10030405

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