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Graph neural networks (GNNs) have shown prominent performance on attributednetwork embedding. However, existing efforts mainly focus on exploiting networkstructures, while the exploitation of node attributes is rather limited as theyonly serve as node features at the initial layer. This simple strategy impedesthe potential of node attributes in augmenting node connections, leading tolimited receptive field for inactive nodes with few or even no neighbors.Furthermore, the training objectives (i.e., reconstructing network structures)of most GNNs also do not include node attributes, although studies have shownthat reconstructing node attributes is beneficial. Thus, it is encouraging todeeply involve node attributes in the key components of GNNs, including graphconvolution operations and training objectives. However, this is a nontrivialtask since an appropriate way of integration is required to maintain the meritsof GNNs. To bridge the gap, in this paper, we propose COllaborative graphNeural Networks--CONN, a tailored GNN architecture for attribute networkembedding. It improves model capacity by 1) selectively diffusing messages fromneighboring nodes and involved attribute categories, and 2) jointlyreconstructing node-to-node and node-to-attribute-category interactions viacross-correlation. Experiments on real-world networks demonstrate that CONNexcels state-of-the-art embedding algorithms with a great margin.

Collaborative Graph Neural Networks for Attributed Network Embedding