Complex Network Measures for Data Classification

Complex networks have become an increasingly relevant research topic in machine learning, with many learning systems in the literature successfully exploring complex network properties and measures. In data classification, the use of complex networks allows the detection of structural and topological patterns related, for example, to the formation pattern of the input data. Some measures of complex networks have already been used in this sense. However, a systematic study capable of characterizing such measures in the context of data classification is lacking in the literature. In this work, we evaluate comparatively the predictive performance of some measures. Specifically, eight complex network measures were selected from the literature, namely: assortativity, average local clustering coefficient, average degree, betweenness, average shortest path length, closeness, global clustering coefficient and eigenvector centrality. For our analyses, both artificial and real-world data sets were considered. The results show that measures such as average shortest path and assortativity, besides presenting high predictive capability, are also more robust to the variation of the network structure. In summary, this research paves a way to support other related works in selecting more appropriate complex network measures for data classification.