Abstract
Graph classification has become an increasingly important research topic in recent years due to its wide applications. However, one interesting problem about how to classify graphs based on the implicit properties of graphs has not been studied yet. To address it, this paper first conducts an extensive study on existing graph theoretical metrics and also propose various novel metrics to discover implicit graph properties. We then apply feature selection techniques to discover a subset of discriminative metrics by considering domain knowledge. Two classifiers are proposed to classify the graphs based on the subset of features. The feasibility of graph classification based on the proposed graph metrics and techniques has been experimentally studied.
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Zhu, L., Ng, W.K., Han, S. (2011). Classifying Graphs Using Theoretical Metrics: A Study of Feasibility. In: Xu, J., Yu, G., Zhou, S., Unland, R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20244-5_6
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DOI: https://doi.org/10.1007/978-3-642-20244-5_6
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