Graph Convolutional Auto-encoder with Bi-decoder and Adaptive-sharing Adjacency
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Graph autoencoder (GAE) serves as an effective unsupervised learning framework to represent graph data in a latent space for network embedding. Most exiting approaches typically focus on minimizing the reconstruction loss of graph structure but neglect the reconstruction of node features, which may result in overfitting due to the capacity of the autoencoders. Additionally, the adjacency matrix in these methods is always fixed such that the adjacency matrix cannot properly represent the connections among nodes in latent space. To solve this problem, in this paper, we propose a novel Graph Convolutional Auto-encoder with Bidecoder and Adaptive-sharing Adjacency method, namely BAGA. The framework encodes the topological structure and node features into latent representations, on which a bi-decoder is trained to reconstruct the graph structure and node features simultaneously. Furthermore, the adjacency matrix can be adaptively updated by the learned latent representations for better representing the connections among nodes in latent space. Experimental results on datasets validate the superiority of our method to the state-of-the-art network embedding methods on the clustering task.
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