Abstract
Community structure in complex networks has been proven to be valuable in a variety of fields, such as biology, social media, health, etc. Researchers have investigated a significant amount of algorithms in complex network analysis and community detection. However, most of them are not expressive to acquire the node and edge representations observed in complex networks. In this paper, we present a new algorithm based on spectral clustering to detect the communities. To improve the performance of the spectral clustering algorithm, we consider an algorithmic framework for learning continuous feature representations for nodes in networks. The proposed algorithm learns a mapping of nodes to low-dimensional space of features that provided a richer representation in learning. The algorithm continues to apply the spectral clustering method to calculate the similarity among any two node embeddings and finish the community detection in the given networks. Experiments show that the proposed algorithm exceeds other state-of-the-art community detection algorithms among various real-world networks from diverse domains and synthetic networks. The algorithm provides a high-quality and accuracy performance in a wide range of data sets.
Published Version
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