Abstract
The clustering coefficient of an unweighted network has been extensively used to quantify how tightly connected is the neighbor around a node and it has been widely adopted for assessing the quality of nodes in a social network. The computation of the clustering coefficient is challenging since it requires to count the number of triangles in the graph. Several recent works proposed efficient sampling, streaming and MapReduce algorithms that allow to overcome this computational bottleneck. As a matter of fact, the intensity of the interaction between nodes, that is usually represented with weights on the edges of the graph, is also an important measure of the statistical cohesiveness of a network. Recently various notions of weighted clustering coefficient have been proposed but all those techniques are hard to implement on large-scale graphs.
In this work we show how standard sampling techniques can be used to obtain efficient estimators for the most commonly used measures of weighted clustering coefficient. Furthermore we also propose a novel graph-theoretic notion of clustering coefficient in weighted networks.
Work partially done while visiting scientist at Google Research NY. Partially supported from Google Focused Award “Algorithms for Large-scale Data Analysis”, EU FET project MULTIPLEX 317532, EU ERC project PAAI 259515.
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Lattanzi, S., Leonardi, S. (2014). Efficient Computation of the Weighted Clustering Coefficient. In: Bonato, A., Graham, F., Prałat, P. (eds) Algorithms and Models for the Web Graph. WAW 2014. Lecture Notes in Computer Science(), vol 8882. Springer, Cham. https://doi.org/10.1007/978-3-319-13123-8_4
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DOI: https://doi.org/10.1007/978-3-319-13123-8_4
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