Research map / Graph learning / Graph algorithms and clustering
Balanced Ranking with Relative Centrality: A multi-core periphery perspective
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Proposes a balanced ranking method based on relative centrality for unsupervised ranking of graph vertices that avoids unbalanced rankings under community structure.
Problem
Traditional graph ranking algorithms can produce unbalanced rankings when the graph has underlying communities, causing information loss, polarised opinions, and reduced diversity.
Approach
Uses a multi-core periphery perspective and a relative centrality notion to produce balanced unsupervised rankings of vertices across communities (abstract only; method details unavailable).
Experimental setup
Builds on: Celis, Straszak & Vishnoi (ICALP 2018)
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