Multi-View Attribute Graph Convolution Networks for Clustering

IJCAI 2020, pp. 2973-2979, 2020.

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We propose a novel Multi-View Attribute Graph Convolution Networks for Clustering, a generally method to multi-view graph neural network

Abstract:

Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods cannot allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly foc...More

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