@inproceedings{1418deaa0bd44edbab31d058ae9e5148,
title = "Connectivity-Based Spectral Sampling for Big Complex Network Visualization",
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.",
author = "Jingming Hu and Hong, {Seok Hee} and Jialu Chen and Marnijati Torkel and Peter Eades and Ma, {Kwan Liu}",
note = "Funding Information: Supported by the ARC Discovery Projects.; 9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020 ; Conference date: 01-12-2020 Through 03-12-2020",
year = "2021",
doi = "10.1007/978-3-030-65347-7_20",
language = "English (US)",
isbn = "9783030653460",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "237--248",
editor = "Benito, {Rosa M.} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, {Luis Mateus} and Marta Sales-Pardo",
booktitle = "Complex Networks and Their Applications IX - Volume 1, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020",
address = "Germany",
}