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Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks
Weights and directionality of the edges carry a large part of the information we can extract from a complex network. However, many measures were formulated initially for undirected binary networks. The necessity to incorporate information about the weights led to the conception of the multiple extensions, particularly for definitions of the local clustering coefficient discussed here. We uncover that not all of these extensions are fully-weighted; some depend on the degree and thus change a lot when an infinitely small weight edge is exchanged for the absence of an edge, a feature that is not always desirable. We call these methods "hybrid" and argue that, in many situations, one should prefer fully-weighted definitions. After listing the necessary requirements for a method to analyze many various weighted networks properly, we propose a fully-weighted continuous clustering coefficient that satisfies all the previously proposed criteria while also being continuous with respect to vanishing weights. We demonstrate that the behavior and meaning of the Zhang--Horvath clustering and our new continuous definition provide complementary results and significantly outperform other definitions in multiple relevant conditions.
@article{item_3320490, title = {{Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks}}, journal = {Physical Review Research}, abstract = {Weights and directionality of the edges carry a large part of the information we can extract from a complex network. However, many measures were formulated initially for undirected binary networks. The necessity to incorporate information about the weights led to the conception of the multiple extensions, particularly for definitions of the local clustering coefficient discussed here. We uncover that not all of these extensions are fully-weighted; some depend on the degree and thus change a lot when an infinitely small weight edge is exchanged for the absence of an edge, a feature that is not always desirable. We call these methods "hybrid" and argue that, in many situations, one should prefer fully-weighted definitions. After listing the necessary requirements for a method to analyze many various weighted networks properly, we propose a fully-weighted continuous clustering coefficient that satisfies all the previously proposed criteria while also being continuous with respect to vanishing weights. We demonstrate that the behavior and meaning of the Zhang--Horvath clustering and our new continuous definition provide complementary results and significantly outperform other definitions in multiple relevant conditions.}, volume = {3}, year = {2021}, slug = {item_3320490}, author = {Fardet, T and Levina, A}, eprint = {2105.06318}, url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043124} }