Online Learning Of Expanding Graphs
Samuel Rey, Bishwadeep Das, Elvin Isufi

TL;DR
This paper introduces a novel online algorithm for inferring expanding network topologies from spatiotemporal signals, addressing challenges of growing graphs and real-time updates in dynamic environments.
Contribution
It presents the first online method for expanding graphs using projected proximal gradient descent with recursive covariance updates and node-specific strategies.
Findings
Effective in real-world epidemic and financial networks
Handles increasing graph size efficiently
Analyzes computational complexity and regret
Abstract
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample…
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Taxonomy
TopicsTeaching and Learning Programming · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
MethodsSparse Evolutionary Training · Focus
