Abstract
Network embedding aims to embed the network into a low-dimensional vector space wherein the structural characteristic of the network and the attribute information of nodes are preserved as much as possible. Many existing network embedding works focused on the homogeneous or heterogeneous plain networks. However, networks in the real world are usually not plain since the nodes in the networks have rich attributes, and these attributes play important roles for encoding nodes’ vector representations. Although some works took into account the attribute information, they could not handle the homogeneous and heterogeneous structure information of the network simultaneously. In order to solve this problem, a new network embedding method that considers both the network’s homogeneous and heterogeneous structure information and nodes attribute information simultaneously is proposed in this paper. The proposed method first obtains nodes attribute information, homogeneous and heterogeneous structure information as three views of the network and learns network embeddings of the three views through different technologies respectively. Then, an attention mechanism is utilized to fuse the embedding results learned from the three views to obtain the final vector representations of nodes. We verify the performance of the proposed model through link prediction tasks on four real-world datasets, and extensive experimental results show that the proposed model outperforms the advanced baselines.
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Notes
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The 8 venues include CVPR, ICCV, ECCV, PAKDD, ECML, NIPS, ICML and EDBT.
- 3.
The 11 venues include KDD, ICDM, SDM, ECIR, SIGIR, AAAI, WWW, IJCAI, VLDB, ICDE and SIGMOD.
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Acknowledgements
This research was supported by Natural Science Foundation of China (Grant no. 61672284), Natural Science Foundation of Jiangsu Province (Grant no. BK20171418), China Postdoctoral Science Foundation (Grant no. 2016M591841), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C).
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Wang, T., Yuan, W., Guan, D. (2021). Attributed Heterogeneous Network Embedding for Link Prediction. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_9
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