Multi-class Graph Clustering via Approximated Effective p-Resistance
24. července 2023
Řečníci
Shota Saito
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Mark Herbster
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O prezentaci
This paper considers the approximation of the effective p-resistance over a graph and its application to multi-class clustering.The effective p-resistance is known to be a metric over a graph. However, it is computationally expensive to obtain the effective p-resistance, and thus it is practically difficult to use.As a solution, we provide an approximation of the effective p-resistance, which is less computationally expensive. We give a theoretical guarantee of the quality of this approximation. We then propose a multi-class clustering algorithm via approximated effective p-resistance. Furthermore, we develop a theoretical connection between effective p-resistance and semi-supervised learning that justifies using effective p-resistance to multi-class clustering.We experimentally demonstrate that our algorithm outperforms the existing spectral clustering using graph p-Laplacian as well as effective 2-resistance-based methods.This paper considers the approximation of the effective p-resistance over a graph and its application to multi-class clustering.The effective p-resistance is known to be a metric over a graph. However, it is computationally expensive to obtain the effective p-resistance, and thus it is practically difficult to use.As a solution, we provide an approximation of the effective p-resistance, which is less computationally expensive. We give a theoretical guarantee of the quality of this approximation.…
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