Soft happy colourings and community structure of networks
Mohammad H. Shekarriz, Dhananjay Thiruvady, Asef Nazari, Rhyd Lewis

TL;DR
This paper explores the relationship between community structures in stochastic block model graphs and soft happy colourings, providing theoretical thresholds, algorithms, and experimental validation.
Contribution
It establishes conditions under which communities induce $ ho$-happy colourings, derives probabilistic thresholds, and develops heuristic algorithms with experimental support.
Findings
Communities induce $ ho$-happy colourings under certain model conditions
A probabilistic threshold for $ ho$-happy colouring is identified
Heuristic algorithms effectively detect community-related colourings
Abstract
For , a -happy vertex in a coloured graph has at least same-colour neighbours, and a -happy colouring (aka soft happy colouring) of is a vertex colouring that makes all the vertices -happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vertices. Graphs with community structures can be modelled by random graph models such as the stochastic block model (SBM). In this paper, we present several theorems showing that both of these notions are related, with numerous real-world applications. We show that, with high probability, communities of graphs in the stochastic block model induce -happy colouring on all vertices if certain conditions on the model parameters are satisfied. Moreover, a probabilistic threshold on is derived so that communities of a graph in…
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Taxonomy
TopicsGene Regulatory Network Analysis
