Abstract
Attributed network embedding (ANE) maps nodes in network into the low-dimensional space while preserving proximities of both node attributes and network topology. Existing methods for ANE integrated node attributes and network topology by three fusion strategies: the early fusion (EF), the synchronous fusion (SF) and the late fusion (LF). In fact, different fusion strategies have their own advantages and disadvantages. In this paper, we develop a dual fusion model named as DFANE. DFANE integrated the EF and the LF into a united framework, where the EF captures the latent complementarity and the LF extracts the distinctive information from node attributes and network topology. Extensive experiments on eight real-world networks have demonstrated the effectiveness and rationality of the DFANE.
Supported by organization of the National Natural Science Foundation of China (61762090, 61262069, 61966036 and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026), the Project of Innovative Research Team of Yunnan Province (2018HC019), Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN), and the National Social Science Foundation of China under Grant No. 18XZZ005.
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Dong, K., Zhou, L., Kong, B., Zhou, J. (2020). A Dual Fusion Model for Attributed Network Embedding. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_8
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DOI: https://doi.org/10.1007/978-3-030-55130-8_8
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