Graph neural networks (GNNs) have shown prominent performance on attributed
network embedding. However, existing efforts mainly focus on exploiting network
structures, while the exploitation of node attributes is rather limited as they
only serve as node features at the initial layer. This simple strategy impedes
the potential of node attributes in augmenting node connections, leading to
limited 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 shown
that reconstructing node attributes is beneficial. Thus, it is encouraging to
deeply involve node attributes in the key components of GNNs, including graph
convolution operations and training objectives. However, this is a nontrivial
task since an appropriate way of integration is required to maintain the merits
of GNNs. To bridge the gap, in this paper, we propose COllaborative graph
Neural Networks--CONN, a tailored GNN architecture for attribute network
embedding. It improves model capacity by 1) selectively diffusing messages from
neighboring nodes and involved attribute categories, and 2) jointly
reconstructing node-to-node and node-to-attribute-category interactions via
cross-correlation. Experiments on real-world networks demonstrate that CONN
excels state-of-the-art embedding algorithms with a great margin.

COllaborative graph Neural Networks (CONN) 是一种专门用于属性网络嵌入的 GNN 体系结构，通过选择性地传播来自相邻节点和相关属性类别的消息，并通过交叉相关联合重构节点之间和节点到属性类别之间的交互来提高模型能力。实验表明 CONN 在真实网络上表现出色，超过了现有的嵌入算法。