  In this paper, we propose an improved version of an agglomerative
hierarchical clustering algorithm that performs disjoint community detection in
large-scale complex networks. The improved algorithm is achieved after
replacing the local structural similarity used in the original algorithm, with
the recently proposed Dynamic Structural Similarity. Additionally, the improved
algorithm is extended to detect fuzzy and crisp overlapping community
structure. The extended algorithm leverages the disjoint community structure
generated by itself and the dynamic structural similarity measures, to compute
a proposed membership probability function that defines the fuzzy communities.
Moreover, an experimental evaluation is performed on reference benchmark graphs
in order to compare the proposed algorithms with the state-of-the-art.
