Clustering in graphs with high clustering coefficients

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Many real world networks possess the so-called small world phenomenon where every node is relatively close to every other node and have a large clustering coefficient, i.e., friends of friends are likely friends. The task of learning an adequate similarity measure on various feature spaces often involves graphs with high clustering coefficients.

We investigate the clustering effect in sparse clustering graphs byexamining the structural and spectral properties as well as the enumeration of patterns. In addition, we consider random graph models for clustering graphs that can be used to analyze the behavior of complex networks.

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