Abstract
Network embedding has attracted lots of attention in recent years. It learns low-dimensional representations for network nodes, which benefits many downstream tasks such as node classification and link prediction. However, most of the existing approaches are designed for a single network scenario. In the era of big data, the related information from different networks should be fused together to facilitate applications. In this paper, we study the problem of fusing the node embeddings and incomplete node attributes provided by different networks into an arbitrary space. Specifically, we first propose a simple but effective inductive method by learning the relationships among node embeddings and the given attributes. Then, we propose its transductive variant by jointly considering the node embeddings and incomplete attributes. Finally, we introduce its deep transductive variant based on deep AutoEncoder. Experimental results on four datasets demonstrate the superiority of our methods.
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Acknowledgment
This work is supported in part by National Natural Science Foundation of China (No. 61902020), Macao Youth Scholars Program (No. AM201912), China Postdoctoral Science Foundation Funded Project (No. 2018M640066), Fundamental Research Funds for the Central Universities (No. FRF-TP-18-016A1), and National Key R&D Program of China (No. 2019QY1402).
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Wang, Z., Cui, J., Chen, Y., Hu, C. (2020). SOLAR: Fusing Node Embeddings and Attributes into an Arbitrary Space. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_27
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