Semi-Local Integration Measure of Node Importance

Tajana Ban Kirigin, Sanda Bujačić Babić and Benedikt Perak
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Tajana Ban Kirigin: Department of Mathematics, University of Rijeka, R. Matejčić 2, 51000 Rijeka, Croatia
Sanda Bujačić Babić: Department of Mathematics, University of Rijeka, R. Matejčić 2, 51000 Rijeka, Croatia
Benedikt Perak: Faculty of Humanities and Social Sciences, University of Rijeka, Sveučilišna Avenija 4, 51000 Rijeka, Croatia

Mathematics, 2022, vol. 10, issue 3, 1-16

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 ( S L I ), 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 S L I 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 S L I 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 S L I measure can improve the results of sentiment analysis. The S L I measure has the potential to be used in various types of complex networks in different research areas.

Keywords: centrality measure; node importance; complex networks; applications of graph data processing; lexical graph analysis; sentiment analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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