Abstract
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.
Research supported by ARC Discovery Project.
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Hu, J., Hong, SH., Eades, P. (2020). Spectral Vertex Sampling for Big Complex Graphs. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_18
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