Abstract
The structural role of a node is an essential network structure information, which provides a better perspective to understand the network structure. In recent years, network embedding learning methods have been widely used in node classification, link prediction, and visualization tasks. Most network embedding learning algorithms attempt to preserve the neighborhood information of nodes. However, these methods are hard to recognize the structural role proximity of nodes. We propose a novel method, RolNE, which learns structural role proximity of nodes through clustering the degree vector of nodes and uses an aggregation function to learn node embedding that contains both neighborhood information and structural role proximity. Experiments on multiple datasets show that our algorithm outperforms other state-of-the-art baselines on downstream tasks.
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Acknowledgment
The research work is supported by the National Key R&D Program with No. 2016QY03D0503, Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040400, National Natural Science Foundation of China (No. 61602474, No. 61602467, No. 61702552).
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Liang, Q. et al. (2020). RolNE: Improving the Quality of Network Embedding with Structural Role Proximity. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_2
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DOI: https://doi.org/10.1007/978-3-030-62005-9_2
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