Abstract
Network immunization has often been conducted by removing nodes with large network centrality so that the whole network can be fragmented into smaller subgraphs. Since contamination (e.g., virus) is propagated among subgraphs (communities) along links in a network, besides centrality, utilization of community structure seems effective for immunization. We have proposed community structure based node scores in terms of a vector representation of nodes in a network. In this paper we report a comparative study of our node scores over both synthetic and real-world networks. The characteristics of the node scores are clarified through the visualization of networks. Extensive experiments are conducted to compare the node scores with other centrality based immunization strategies. The results are encouraging and indicate that the node scores are promising.
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Keywords
- Community Structure
- Betweenness Centrality
- Vector Representation
- Network Immunization
- Eigenvector Centrality
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Yamada, Y., Yoshida, T. (2012). A Comparative Study of Community Structure Based Node Scores for Network Immunization. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_33
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DOI: https://doi.org/10.1007/978-3-642-35236-2_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35235-5
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