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Structure-guided attributed network embedding with “centroid” enhancement

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Abstract

Attributed network embedding aims at learning low-dimensional network representations in terms of both network structure and attribute information. Most existing methods deal with network structure and attributes separately and combine them in particular ways, which weaken the affinity between structure and attributes and thus lead to suboptimal performance. Moreover, some methods focus solely on local or global network structure, without fully utilizing the structure information underling the network. To address these limitations, we propose structure-guided attributed network embedding with “centroid” enhancement, an unsupervised approach to embed network structure and attribute information comprehensively and seamlessly. Specifically, we regard the neighborhood of each node as a “cluster” and calculate a “centroid” for it through graph convolutional network. We design a “centroid”-based triplet regularizer to impose a gap constraint inspired by K-means. A “centroid”-augment skip-gram model is utilized to deal with high-order proximity. By jointly optimizing the two objectives, the learned representation can preserve both local-global network structure and attribute information. Throughout the model, we exploit network structure to guide the aggregation of attributes, and thus effectively captures the affinity between them. Experimental results on eight real-world datasets demonstrate the superiority of our model over the state-of-the-art methods.

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Notes

  1. https://github.com/DLUTElvis/SANECE.

  2. https://linqs.soe.ucsc.edu/data.

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Acknowledgements

This work was partially supported by the National Science Foundation of China (Grant No. 61632019), the Fundamental Research Funds for the Central Universities (Grant No. DUT19RC(3)048 and DUT20GF106) and the JSPS Grant-in-Aid for Early-Career Scientists (Grant No. 19K20352).”

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, China

    Zihan Liao, Wenxin Liang & Beilei Cui

  2. Data Platform Research Team, Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Xin Liu

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  1. Zihan Liao
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  2. Wenxin Liang
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  3. Beilei Cui
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  4. Xin Liu
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Correspondence to Wenxin Liang.

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Liao, Z., Liang, W., Cui, B. et al. Structure-guided attributed network embedding with “centroid” enhancement. Computing 103, 1599–1620 (2021). https://doi.org/10.1007/s00607-021-00916-y

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