Abstract
Complex network analysis has gained research interests in a wide range of fields. Network motif, which is one of the most popular network properties, is a statistically significant network subgraph. In this paper, we propose a fast methodology, called Four-node Motif Detection Algorithm (FMDA), to extract four-node motifs in complex networks. Specifically, we employ a two-way spectral clustering method to cut big networks into small sub-graphs, and then identify motifs by recognition algorithm to reduce the computational complexity. After that, we use three isomorphic four-node motifs to analyze network structure by American Physical Society (APS) data set.
Keywords
- Network Structure Analysis
- Fast Methodology
- Collaboration Relationships
- Academic Network
- Network Motif Discovery
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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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61572106, 61502075); the Fundamental Research Funds for the Central Universities (DUT17RC(4)49); China Postdoctoral Science Foundation Funded Project (2015M580224); Liaoning Province Doctor Startup Fund (201501166).
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Ning, Z., Liu, L., Yu, S., Xia, F. (2018). Detection of Four-Node Motif in Complex Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_37
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DOI: https://doi.org/10.1007/978-3-319-72150-7_37
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