Group Centrality for Semantic Networks: a SWOT analysis featuring Random Walks

TL;DR

This paper analyzes the use of group centrality measures, especially random walks, in semantic networks, highlighting their importance, computational challenges, and potential applications through a SWOT framework.

Contribution

It provides a comprehensive SWOT analysis of group centrality in semantic networks, emphasizing the role of random walks and NP-hardness of finding optimal central sets.

Findings

Random walks are prominent in group centrality measures.

Finding the most central set is NP-hard.

SWOT analysis reveals strengths, weaknesses, opportunities, and threats.

Abstract

Group centrality is an extension of the classical notion of centrality for individuals, to make it applicable to sets of them. We perform a SWOT (strengths, weaknesses, opportunities and threats) analysis of the use of group centrality in semantic networks, for different centrality notions: degree, closeness, betweenness, giving prominence to random walks. Among our main results stand out the relevance and NP-hardness of the problem of finding the most central set in a semantic network for an specific centrality measure.