Abstract:Graph embedding learns low-dimensional vector representations which capture and preserve information in original graphs. Common shallow neural networks and deep autoencoder only use adjacency matrix as input, and usually ignore node attributes and features. Shallow graph neural networks cannot spread the node characteristic information on a large scale. Many deep models suffer the problem of oversmoothing. Therefore, these methods can't fully incorporate network information. In this paper, we propose a novel S… Show more
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