  Based on an expert systems approach, the issue of community detection can be
conceptualized as a clustering model for networks. Building upon this further,
community structure can be measured through a clustering coefficient, which is
generated from the number of existing triangles around the nodes over the
number of triangles that can be hypothetically constructed. This paper provides
a new definition of the clustering coefficient for weighted networks under a
generalized definition of triangles. Specifically, a novel concept of triangles
is introduced, based on the assumption that, should the aggregate weight of two
arcs be strong enough, a link between the uncommon nodes can be induced. Beyond
the intuitive meaning of such generalized triangles in the social context, we
also explore the usefulness of them for gaining insights into the topological
structure of the underlying network. Empirical experiments on the standard
networks of 500 commercial US airports and on the nervous system of the
Caenorhabditis elegans support the theoretical framework and allow a comparison
between our proposal and the standard definition of clustering coefficient.
