Abstract
Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph.
This paper introduces a new divide and conquer approach to spectral graph sampling based on the graph connectivity (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling and spectral edge sampling by computing effective resistance values of vertices and edges for each connected component. Experimental results demonstrate that our new connectivity-based spectral sampling approach is significantly faster than previous methods, while preserving the same sampling quality.
Supported by the ARC Discovery Projects.
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Hu, J., Hong, SH., Chen, J., Torkel, M., Eades, P., Ma, KL. (2021). Connectivity-Based Spectral Sampling for Big Complex Network Visualization. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_20
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DOI: https://doi.org/10.1007/978-3-030-65347-7_20
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