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Structural iterative lexicographic autoencoded node representation

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Abstract

Graph representation learning approaches are effective to automatically extract relevant hidden features from graphs. Previous related work in graph representation learning can be divided into connectivity and structural-based. Connectivity-based representation learning methods work on the assumption that neighboring nodes should have similar representations. While structural node representation learning assumes that nodes with the same structure should have identical representations; structural representation learning is suitable for node classification and regression tasks. Possible drawbacks of current structural node representation learning approaches are prohibitive execution time complexity and the inability to entirely preserve structural information. In this work, we propose SILA, a Structural Iterative Lexicographic Autoencoded approach for node representation learning. This new iterative approach presents a small number of iterations, and compared with the method presented in the literature, shows better performance in preserving structural information for both classification and regression tasks.

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Notes

  1. The code will be made publicly available after paper acceptance.

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Acknowledgements

This research was funded by a National Centers of Academic Excellence in Cybersecurity grant (H98230-22-1-0300), which is part of the National Security Agency.

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  1. Computer Science Department, Boise State University, 777 W Main St, Boise, ID, 83702, USA

    Mikel Joaristi & Edoardo Serra

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  1. Mikel Joaristi
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  2. Edoardo Serra
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Correspondence to Edoardo Serra.

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Joaristi, M., Serra, E. Structural iterative lexicographic autoencoded node representation. Data Min Knowl Disc 37, 289–317 (2023). https://doi.org/10.1007/s10618-022-00880-x

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