Abstract
Information Propagation between different communities in social networks are often through structural hole spanners, which makes that detecting structural hole spanners in social networks has received great attention in recent years. Existing SH spanners detection methods usually rely on graph theory knowledge. However, these methods have obvious drawbacks of poor performance and expensive computation for largescale networks. In this work, we propose a novel solution to identify SH spanner based on graph embedding learning. Considering the special topological nature of SH spanner in the network, we design a deep embedding learning method for detecting SH spanner. Extensive experimental results on real world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the SH spanner identification task in terms of several metrics.
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Acknowledgments
This work is funded by the National Natural Science Foundation of China under numbers 61972135 and 61602159, Natural Science Foundation of Heilongjiang Province under number F201430, Innovation Talents Project of Science and Technology Bureau of Harbin under numbers 2017RAQXJ094 and 2017RAQXJ131, Fundamental Research Funds of Universities in Heilongjiang Province, Special Fund of Heilongjiang University under numbers HDJCCX-201608, KJCX201815, and KJCX201816.
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Chen, S., Quan, Z., Liu, Y. (2019). Identifying Structural Hole Spanners in Social Networks via Graph Embedding. In: Li, Y., Cardei, M., Huang, Y. (eds) Combinatorial Optimization and Applications. COCOA 2019. Lecture Notes in Computer Science(), vol 11949. Springer, Cham. https://doi.org/10.1007/978-3-030-36412-0_8
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DOI: https://doi.org/10.1007/978-3-030-36412-0_8
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