A small world network is characterized by a small average shortest path length, and a large clustering coefficient.
Small-worldness is commonly measured with the coefficient sigma or omega.
Both coefficients compare the average clustering coefficient and shortest path length of a given graph against the same quantities for an equivalent random or lattice graph.
For more information, see the Wikipedia article on small-world network .
<Unimplemented 'footnote' '.. [1] Small-world network:: https://en.wikipedia.org/wiki/Small-world_network'>
Functions for estimating the small-world-ness of graphs.
Functions for estimating the small-world-ness of graphs.
A small world network is characterized by a small average shortest path length, and a large clustering coefficient.
Small-worldness is commonly measured with the coefficient sigma or omega.
Both coefficients compare the average clustering coefficient and shortest path length of a given graph against the same quantities for an equivalent random or lattice graph.
For more information, see the Wikipedia article on small-world network .
<Unimplemented 'footnote' '.. [1] Small-world network:: https://en.wikipedia.org/wiki/Small-world_network'>
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them