This paper introduces a new vertex sampling method for big complex graphs, based on the spectral sparsification, a technique to reduce the number of edges in a graph while retaining its structural properties. More specifically, our method reduces the number of vertices in a graph while retaining its structural properties, based on the high effective resistance values. Extensive experimental results using graph sampling quality metrics, visual comparison and shape-based metrics confirm that our new method significantly outperforms the random vertex sampling and the degree centrality based sampling.
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