  Small-world networks, i.e. networks displaying both a high clustering
coefficient and a small characteristic path length, are obliquitous in nature.
Since their identification, the "small-worldness" metric, as proposed by
Humphries and Gurney, has frequently been used to detect such structural
property in real-world complex networks, to a large extent in the study of
brain dynamics. Here I discuss several of its drawbacks, including its lack of
definition in disconnected networks and the impossibility of assessing a
statistical significance; and present different alternative formulations to
overcome these difficulties, validated through the phenospaces representing a
set of 48 real networks.
