Randomized Spectral Clustering for Large-Scale Multi-Layer Networks

TL;DR

This paper introduces a fast, randomized spectral clustering algorithm for large multi-layer networks that reduces computational costs while maintaining accuracy, enabling efficient community detection at scale.

Contribution

The paper presents a novel randomized spectral clustering method for multi-layer networks that improves efficiency and scalability without sacrificing accuracy.

Findings

The algorithm achieves low time complexity and saves storage space.

Misclassification error bounds are maintained under certain conditions.

Numerical studies demonstrate rapid clustering on networks with millions of nodes.

Abstract

Large-scale multi-layer networks with large numbers of nodes, edges, and layers arise across various domains, which poses a great computational challenge for the downstream analysis. In this paper, we develop an efficient randomized spectral clustering algorithm for community detection of multi-layer networks. We first utilize the random sampling strategy to sparsify the adjacency matrix of each layer. Then we use the random projection strategy to accelerate the eigen-decomposition of the sum-of-squared sparsified adjacency matrices of all layers. The communities are finally obtained via the k-means of the eigenvectors. The algorithm not only has low time complexity but also saves the storage space. Theoretically, we study the misclassification error rate of the proposed algorithm under the multi-layer stochastic block models, which shows that the randomization does not deteriorate the error bound under certain conditions. Numerical studies on multi-layer networks with millions of nodes show the superior efficiency of the proposed algorithm, which achieves clustering results rapidly. A new R package called MLRclust is developed and made available to the public.