  An essential feature of large scale free graphs, such as the Web,
protein-to-protein interaction, brain connectivity, and social media graphs, is
that they tend to form recursive communities. The latter are densely connected
vertex clusters exhibiting quick local information dissemination and
processing. Under the fuzzy graph model vertices are fixed while each edge
exists with a given probability according to a membership function. This paper
presents Fuzzy Walktrap and Fuzzy Newman-Girvan, fuzzy versions of two
established community discovery algorithms. The proposed algorithms have been
applied to a synthetic graph generated by the Kronecker model with different
termination criteria and the results are discussed.
