Conference Proceedings

Graph clustering in all parameter regimes

Junhao Gan, DF Gleich, N Veldt, Anthony Wirth, Xin Zhang

Leibniz International Proceedings in Informatics, LIPIcs | Schloss Dagstuhl | Published : 2020


Resolution parameters in graph clustering control the size and structure of clusters formed by solving a parametric objective function. Typically there is more than one meaningful way to cluster a graph, and solving the same objective function for different resolution parameters produces clusterings at different levels of granularity, each of which can be meaningful depending on the application. In this paper, we address the task of efficiently solving a parameterized graph clustering objective for all values of a resolution parameter. Specifically, we consider a new analysis-friendly objective we call LambdaPrime, involving a parameter λ ∈ (0, 1). LambdaPrime is an adaptation of LambdaCC, a..

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Awarded by Australian Research Council