Abstract
This paper studies the problem of learning representations for network. Existing approaches embed vertices into a low dimensional continuous space which encodes local or global network structures. While these methods show improvements over traditional representations on node classification tasks, they ignore label information until the learnt embeddings are used for training classifier. That is, the process of representation learning is separated from the labels and lacks such information.
In this paper, we propose a novel method which learns the embeddings for vertices under the supervision of labels. Motivated by the idea of label propagation, our approach extends the traditional label propagation to the deep neural network field. The embedding of a node could contain the structural and label information by broadcasting the label information during the training process. We conduct extensive experiments on two real network datasets. Results demonstrate that our approach outperforms both the state-of-the-art graph embedding and label propagation approaches by a large margin.
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Acknowledgments
The work described in this paper has been supported in part by the NSFC projects (61572376), and the 111 project (B07037).
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Chen, Y., Qian, T. (2017). SLICE: Structural and Label Information Combined Embedding for Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_8
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