  Concepts and methods of complex networks have been employed to uncover
patterns in a myriad of complex systems. Unfortunately, the relevance and
significance of these patterns strongly depends on the reliability of the data
sets. In the study of collaboration networks, for instance, unavoidable noise
pervading author's collaboration datasets arises when authors share the same
name. To address this problem, we derive a hybrid approach based on authors'
collaboration patterns and on topological features of collaborative networks.
Our results show that the combination of strategies, in most cases, performs
better than the traditional approach which disregards topological features. We
also show that the main factor for improving the discriminability of homonymous
authors is the average distance between authors. Finally, we show that it is
possible to predict the weighting associated to each strategy compounding the
hybrid system by examining the discrimination obtained from the traditional
analysis of collaboration patterns. Once the methodology devised here is
generic, our approach is potentially useful to classify many other networked
systems governed by complex interactions.
