Abstract
Network embedding, which learns a low-dimensional representation for each node in a network, has been proved to be highly effective for a variety of downstream tasks. In this paper, we propose a novel Fusion-based Attributed Network Embedding framework (FANE), which consists of two modules. The first is the feature-learning module, in which we propose a general and scalable method SparseAE to embed different types of information (structure, attribute, etc.) into separate low-dimensional vectors. The second is the feature-fusion module, which learns the fused embedding vector to capture the underlying relationships between different types of information for downstream prediction tasks. Extensive experiments on multiple real-world datasets show that our method can outperform several state-of-the-art methods in many downstream tasks, including node classification and link prediction.
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Acknowledgement
The work was supported by grants of the Natural Science Foundation 61972291, and the National Key Research and Development Project of China 2020AAA0108505.
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Li, G., Li, Q., Liu, J., Zhu, Y., Zhong, M. (2021). FANE: A Fusion-Based Attributed Network Embedding Framework. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_5
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DOI: https://doi.org/10.1007/978-3-030-85896-4_5
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