Many networks exhibit the small-world property of the neighborhood connectivity being higher than in comparable random networks. However, the standard measure of local neighborhood clustering is typically not defined if a node has one or no neighbor . In such cases, local clustering has traditionally been set to zero and the node was included in the global clustering coefficient. Such a procedure leads to under-estimation of the neighborhood clustering in sparse networks. We propose to include \theta as the proportion of leafs and isolated nodes to estimate the contribution of these cases to the mean clustering value. Furthermore, we provide a formula for estimating a clustering coefficient that excludes these undefined cases . This novel definition leads to values which are up to 140\% higher than the traditional values for the observed networks indicating that neighborhood connectivity is normally underestimated. We find that the definition of the clustering coefficient has a major effect when comparing different networks. For metabolic networks of URLanisms, relations changed for 58\% of the comparisons when a different definition was applied. Values for the clustering coefficient vary with the ratio of isolated and leaf nodes which is critical for comparing networks. We suggest including ratio and definition in publications to enable comparisons across different studies .
Many networks exhibit the small-world property of the neighborhood connectivity being higher than in comparable random networks. However, the standard measure of local neighborhood clustering is typically not defined if a node has one or no neighbors . In such cases, local clustering has traditionally been set to zero and this value influenced the global clustering coefficient. Such a procedure leads to underestimation of the neighborhood clustering in sparse networks. We propose to include \theta as the proportion of leafs and isolated nodes to estimate the contribution of these cases and provide a formula for estimating a clustering coefficient excluding these cases from the Watts and Strogatz (1998 Nature 393 440-2) definition of the clustering coefficient. Excluding leafs and isolated nodes leads to values which are up to 140\% higher than the traditional values for the observed networks indicating that neighborhood connectivity is normally underestimated. We find that the definition of the clustering coefficient has a major effect when comparing different networks. For metabolic networks of URLanisms, relations changed for 58\% of the comparisons when a different definition was applied. We also show that the definition influences small-world features and that the classification can change from non-small-world to small-world network. We discuss the use of an alternative measure, disconnectedness D, which is less influenced by leafs and isolated nodes .
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the node was included in the
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to the mean clustering value. Furthermore, we
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that excludes these undefined cases . This novel definition
excluding these cases from the Watts and Strogatz (1998 Nature 393 440-2) definition of the clustering coefficient. Excluding leafs and isolated nodes
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Values for the clustering coefficient vary with the ratio of isolated and leaf nodes which is critical for comparing networks. We suggest including ratio and definition in publications to enable comparisons across different studies
We also show that the definition influences small-world features and that the classification can change from non-small-world to small-world network. We discuss the use of an alternative measure, disconnectedness D, which is less influenced by leafs and isolated nodes