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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 67; no. 9; pp. 2471 - 2483
Main Authors: Shen, Yanning, Leus, Geert, Giannakis, Georgios B.
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2904922