DNC: A Deep Neural Network-based Clustering-oriented Network Embedding Algorithm

Abstract

Deep Neural Networks (DNNs) have achieved impressive success in the domain of Euclidean data such as image. However, designing deep neural network to cluster nodes especially in social networks is still a challenging task. Moreover, recent advanced methods for node clustering have focused on learning node embedding, upon which classic clustering algorithms like K-means are applied. Nevertheless, the resulting node embeddings are customarily task-agnostic. This results in the fact that the performance of clustering is difficult to guarantee. To effectively mitigate the problem, in this paper, we propose a novel clustering-oriented node embedding method named Deep Node Clustering (DNC) for non-attributed network data by resorting to deep neural networks. We first present a preprocessing method via adopting a random surfing model to capture graph structural information directly. Subsequently, we propose to learn a deep clustering network, which could jointly learn node embeddings and cluster assignments. Extensive experiments on three real-world network datasets for node clustering are conducted, which demonstrate that the proposed DNC substantially outperforms the state-of-the-art node clustering methods.