Online Graph-Adaptive Learning With Scalability and Privacy

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 unavai...

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Vydáno v:	IEEE transactions on signal processing Ročník 67; číslo 9; s. 2471 - 2483
Hlavní autoři:	Shen, Yanning, Leus, Geert, Giannakis, Georgios B.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive learning Companies Complex systems Distance learning Estimation Graph signal reconstruction Kernel kernel-based learning learning over dynamic graphs Nodes online learning Privacy Real time Real-time systems Social networking (online) Social networks Task analysis
ISSN:	1053-587X, 1941-0476
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Abstract	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 context, 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.
AbstractList	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 context, 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.
Author	Giannakis, Georgios B. Shen, Yanning Leus, Geert
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SubjectTerms	Adaptive learning Companies Complex systems Distance learning Estimation Graph signal reconstruction Kernel kernel-based learning learning over dynamic graphs Nodes online learning Privacy Real time Real-time systems Social networking (online) Social networks Task analysis
Title	Online Graph-Adaptive Learning With Scalability and Privacy
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