Several previous works proposed techniques to detect outliers in graph
data. Usually, some complex dataset is modeled as a graph and a technique for detecting outliers in graphs is applied. The impact of the graph model on the outlier
detection capabilities of any method has been ignored. Here we assess the impact
of the graph model on the outlier detection performance and the gains that may be
achieved by using multiple graph models and combining the results obtained by
these models. We show that assessing the similarity between graphs may be a guidance to determine effective combinations, as less similar graphs are complementary
with respect to outlier information they provide and lead to better outlier detection.