Which Algorithm Performs Best: Algorithm Selection for Community Detection

Abstract

A myriad of community detection methods have been designed to discover communities based on specific network features in different disciplines, such as sociology, physics, and computer science. Consequentially, we have to face the problem of Algorithm Selection for Community Detection (ASCD): Given a specific network, which algorithm should we select to reveal its latent community structures In this study, we propose a model called CYDES to address the ASCD problem. CYDES consists of two parts, namely feature matrix generation and algorithm classification. We combine three effective feature extraction methods with the idea of BOW model to construct a fixed-size feature matrix. After a nonlinear transformation to the feature matrix, a softmax regression model is utilized to generate a classification label representing the best community detection algorithm we select. Extensive experimental results demonstrate that CYDES has high algorithm selection quality for community detection in networks.