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arXiv · 2108.09128

Semi-supervised Network Embedding with Differentiable Deep Quantisation

Tao He·Lianli Gao·Jingkuan Song·Yuan-Fang Li
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Abstract

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge. Building on our previous work on semi-supervised network embedding, we develop d-SNEQ, a differentiable DNN-based quantisation method for network embedding. d-SNEQ incorporates a rank loss to equip the learned quantisation codes with rich high-order information and is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed. We also propose a new evaluation metric, path prediction, to fairly and more directly evaluate model performance on the preservation of high-order information. Our evaluation on four real-world networks of diverse characteristics shows that d-SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, path prediction, node classification, and node recommendation while being far more space- and time-efficient.

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Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li. 2021-08-20. Semi-supervised Network Embedding with Differentiable Deep Quantisation. https://doi.org/10.1109/tnnls.2021.3129280

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