  for representing, characterizing, and modeling an ample range of structures
and phenomena from both theoretical and applied perspectives. The present work
describes the application of the recently introduced real-valued Jaccard and
coincidence similarity indices for building complex networks from datasets.
More specifically, two nodes are linked whenever their similarity is greater
than a given threshold. Weighted networks can also be obtained by taking the
similarity indices as weights. It is shown that the proposed approach allows
substantially enhanced performance when compared to cosine distance-based
approaches, yielding a detailed description of the specific patterns of
connectivity between the nodes. The impressive ability of the proposed
methodology to emphasize the modular structure of networks is also illustrated
with respect to the iris dataset and C.~elegans neuronal network connectivity
data with remarkable results. The reported methodology and results pave the way
to a significant number of possible theoretical and applied developments.
