  Complex networks refer to large-scale graphs with nontrivial connection
patterns. The salient and interesting features that the complex network study
offer in comparison to graph theory are the emphasis on the dynamical
properties of the networks and the ability of inherently uncovering pattern
formation of the vertices. In this paper, we present a hybrid data
classification technique combining a low level and a high level classifier. The
low level term can be equipped with any traditional classification techniques,
which realize the classification task considering only physical features (e.g.,
geometrical or statistical features) of the input data. On the other hand, the
high level term has the ability of detecting data patterns with semantic
meanings. In this way, the classification is realized by means of the
extraction of the underlying network's features constructed from the input
data. As a result, the high level classification process measures the
compliance of the test instances with the pattern formation of the training
data. Out of various high level perspectives that can be utilized to capture
semantic meaning, we utilize the dynamical features that are generated from a
tourist walker in a networked environment. Specifically, a weighted combination
of transient and cycle lengths generated by the tourist walk is employed for
that end. Interestingly, our study shows that the proposed technique is able to
further improve the already optimized performance of traditional classification
techniques.
