  Complex systems are usually represented as an intricate set of relations
between their components forming a complex graph or network. The understanding
of their functioning and emergent properties are strongly related to their
structural properties. The finding of structural patterns is of utmost
importance to reduce the problem of understanding the structure-function
relationships. Here we propose the analysis of similarity measures between
nodes using hierarchical clustering methods. The discrete nature of the
networks usually leads to a small set of different similarity values, making
standard hierarchical clustering algorithms ambiguous. We propose the use of
"multidendrograms", an algorithm that computes agglomerative hierarchical
clusterings implementing a variable-group technique that solves the
non-uniqueness problem found in the standard pair-group algorithm. This problem
arises when there are more than two clusters separated by the same maximum
similarity (or minimum distance) during the agglomerative process. Forcing
binary trees in this case means breaking ties in some way, thus giving rise to
different output clusterings depending on the criterion used. Multidendrograms
solves this problem grouping more than two clusters at the same time when ties
occur.
