Abstract
Graph embedding is an effective yet efficient way to convert graph data into a low dimensional space. In recent years, deep learning has applied on graph embedding and shown outstanding performance. Adjacency matrix is often taken as the storage data structure of graph. However, there are the problems of insufficient spatial proximity information in adjacency matrix. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial proximity matrix can obtain subspace of the graph which can help convolution neural network easily and quickly extract the spatial localization. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other deep learning methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.
Highlights
Graph is an important data representation of complex networks
This study proposes a spatial feature extraction method based on auto-encoder and convolutional neural network to extract the spatial features of the reconstructed adjacency matrix
Due to the convolutional neural network as an excellent deep learning model for spatial analyses, this study proposes an auto-encoder based on convolutional neural network to automatically extract the spatial features of the reconstructed adjacency matrix for social networks
Summary
Graph is an important data representation of complex networks. Effective community detection provides users a deeper understanding of networks, and it can benefit a lot of useful applications such as node classification, node recommendation, link prediction etc. Analogous to image-based convolutional networks that operate on locally connected regions of the input, the study presents a reconstruction approach of adjacency matrix to storage spatial proximity between nodes. VOLUME 8, 2020 topology, the proposed matrix reconstruction method based on a novel cyberspace structure reconstruction strategy is applied to find the opinion leader in the social network and find the nearer neighbors for reconstructing adjacency matrix with the spatial proximity matrix. This study proposes a spatial feature extraction method based on auto-encoder and convolutional neural network to extract the spatial features of the reconstructed adjacency matrix. (1) A matrix reconstruction method based on a novel cyberspace structure reconstruction strategy is proposed to obtain the spatial proximity matrices of social networks, which can obtain subspace of the graph and help convolution neural network and quickly extract the spatial localization.
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