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Dynamic Spectral Clustering with Provable Approximation Guarantee
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:25844-25870, 2024.
Abstract
This paper studies clustering algorithms for dynamically evolving graphs {Gt}, in which new edges (and potential new vertices) are added into a graph, and the underlying cluster structure of the graph can gradually change. The paper proves that, under some mild condition on the cluster-structure, the clusters of the final graph GT of nT vertices at time T can be well approximated by a dynamic variant of the spectral clustering algorithm. The algorithm runs in amortised update time O(1) and query time o(nT). Experimental studies on both synthetic and real-world datasets further confirm the practicality of our designed algorithm.