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Community preserving network embedding

Community preserving network embedding

Wang, Xiao, Cui, Peng, Wang, Jing, Pei, Jian, Zhu, Wenwu and Yang, Shiqiang (2017) Community preserving network embedding. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). AAAI Press, pp. 203-209.

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Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Uncontrolled Keywords: network embedding, nonnegative matrix factorization, unsupervised learnng
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 14 Jan 2021 10:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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