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Structural hole detection based on weighted meta path in heterogeneous networks

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Abstract

With the rapid development of online social networks, the detection of structural holes, i.e. identifying the key nodes that can bridge with individuals or groups without direct relationship in social networks, has attracted more attention of a large number of researches. The existing researches mainly focus on the influence of a homogeneous network structure, ignoring the importance of node types and different edges in online social networks. In this paper, an algorithm based on weighted meta paths for detecting structural hole in heterogeneous networks (SH_WMP) is proposed. SH_WMP not only flexibly integrates rich semantic information of heterogeneous networks, but also utilizes edge weight and potential link information to improve the performance. Experimental results show that the proposed method outperforms the comparison methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61762090, 61262069, 61472346, and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026, 2015FB114), the Project of Innovative Research Team of Yunnan Province (2018HC019), and Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN), the Education Department Foundation of Yunnan Province (2019J0005, 2019Y0006).

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Authors and Affiliations

  1. School of Information, Yunnan University, Kunming, 650091, Yunnan, People’s Republic of China

    Yudi Yang, Jingjing Zhang, Yuexing Chen, Lihua Zhou & Bing Kong

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  1. Yudi Yang
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  2. Jingjing Zhang
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  3. Yuexing Chen
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  4. Lihua Zhou
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Correspondence to Lihua Zhou.

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Yang, Y., Zhang, J., Chen, Y. et al. Structural hole detection based on weighted meta path in heterogeneous networks. Evol. Intel. 13, 211–220 (2020). https://doi.org/10.1007/s12065-019-00342-2

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