engleski

Semi-Local Integration Centrality for Complex Networks

Centrality is one of the fundamental concepts in graph theory and network analysis. Numerous centrality measures have been introduced to reflect various properties of complex networks such as connectivity, survivability, and robustness, and attempt to numerically evaluate the importance of nodes in a network. In this work, we introduce Semi-Local Integration (SLI), which evaluates the integration of nodes within their neighbourhood. This centrality measure evaluates the importance of nodes according to how integrated they are in the local subnetwork. The measure considers both the weighted degree centrality of the node itself and the weighted degree of the adjacent nodes, as well as the number of cycles that are part of the neighbouring subnetwork of the node itself. SLI centrality is particularly suitable for applications in dynamic and complex networks, where it could optimize the analysis of subnetwork structures, including friend-of-a-friend (FoF)- based networks such as social networks. We demonstrate the potential of applications of the SLI measure in the analysis of lexical networks, which form the basis of many natural language processing (NLP) tasks. The Python function implementing the SLI measure is available in the GitHub repository

centrality measure ; node importance ; complex networks ; applications of graph data processing ; lexical graph analysis

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