Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In this con, this paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed, which is scalable to large-size networks. Unlike most existing methods that re-solve the function estimation problem over all existing nodes whenever a new node joins the network, the novel method is capable of providing real-time evaluation of the function values on newly joining nodes without resorting to a batch solver. Interestingly, the novel scheme only relies on an encrypted version of each node's connectivity in order to learn the nodal attributes, which promotes privacy. Experiments on both synthetic and real datasets corroborate the effectiveness of the proposed methods.
Bibliographical noteFunding Information:
Manuscript received August 3, 2018; revised December 2, 2018; accepted February 20, 2019. Date of publication March 13, 2019; date of current version April 4, 2019. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gwo Giun Lee. This work was supported in part by the National Science Foundation under NSF 1711471 and 1500713 and in part by the National Institutes of Health under NIH 1R01GM104975-01. (Corresponding author: Georgios B. Giannakis.) Y. Shen and G. B. Giannakis are with the Department of ECE and the Digital Technology Center, University of Minnesota, Minneapolis, MN 55455 USA (e-mail:,firstname.lastname@example.org; email@example.com).
This work was supported in part by the National Science Foundation under NSF 1711471 and 1500713 and in part by the National Institutes of Health under NIH 1R01GM104975-01.
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- Graph signal reconstruction
- kernel-based learning
- learning over dynamic graphs
- online learning