Network Sampling Based on Centrality Measures for Relational Classification
Many real-world networks, such as the Internet, social networks, biological networks, and others, are massive in size, which impairs their processing and analysis. To cope with this, the network size could be reduced without losing relevant information. In this paper, we extend a work that proposed a sampling method based on the following centrality measures: degree, k-core, clustering, eccentricity and structural holes. For our experiments, we remove \(30\%\) and \(50\%\) of the vertices and their edges from the original network. After, we evaluate our proposal on six real-world networks on relational classification task using eight different classifiers. Classification results achieved on sampled graphs generated from our proposal are similar to those obtained on the entire graphs. The execution time for learning step of the classifier is shorter on the sampled graph compared to the entire graph and random sampling. In most cases, the original graph was reduced by up to \(50\%\) of its initial number of edges without losing topological properties.
KeywordsNetwork sampling Relational classification Centrality measures Missing data Complex networks
This work was partially supported by the São Paulo Research Foundation (FAPESP) grants: \(2013/12191-5\) and \(2015/14228-9\), National Council for Scientific and Technological Development (CNPq) grants: \(140688/2013-7\) and \(302645/2015-2\), and Coordination for the Improvement of Higher Education Personnel (CAPES), Brazil.
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