Abstract
Network Embedding, which represents nodes in networks with efficient low-dimensional vectors, has been proved useful in a variety of applications. However, most existing approaches study single-view networks but not the multi-view networks with multiple types of relationships between nodes. Meanwhile, they ignore the rich features associated with the nodes, which is common in real world. In this paper, we propose a novel network embedding method, Intra-view and Inter-view attention for Multi-view Network Embedding (I2MNE), which leverages both the multi-view network structure and the node features to efficiently generate node representations. Specially, we introduce the intra-view attention when aggregating node features from neighbors for each single view and the inter-view attention when integrating representations across different views. Experiments on two real-world networks show that our approach outperforms other counterpart network embedding methods.
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1. IEEE Trans. Parallel Distrib. Syst. 2. STOC 3. IEEE Communications Magazine 4. ACM Trans. Graph. 5. CHI 6. ACL 7. CVPR 8. WWW.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (Nos. U1509206, 61625107, U1611461), the Key Program of Zhejiang Province, China (No. 2015C01027).
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Wang, Y., Hu, L., Zhuang, Y., Wu, F. (2018). Intra-view and Inter-view Attention for Multi-view Network Embedding. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_19
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