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Graph measures that express closeness or distance between nodes can beemployed for graph nodes clustering using metric clustering algorithms. Thereare numerous measures applicable to this task, and which one performs better isan open question. We study the performance of 25 graph measures on generatedgraphs with different parameters. While usually measure comparisons are limitedto general measure ranking on a particular dataset, we aim to explore theperformance of various measures depending on graph features. Using an LFR graphgenerator, we create a dataset of 11780 graphs covering the whole LFR parameterspace. For each graph, we assess the quality of clustering with k-meansalgorithm for each considered measure. Based on this, we determine the bestmeasure for each area of the parameter space. We find that the parameter spaceconsists of distinct zones where one particular measure is the best. We analyzethe geometry of the resulting zones and describe it with simple criteria. Givenparticular graph parameters, this allows us to recommend a particular measureto use for clustering.

Dissecting graph measure performance for node clustering in LFR parameter space