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.

翻译：图神经网络（GNNs）在属性网络嵌入任务中展现出卓越性能。然而，现有研究主要聚焦于网络结构的挖掘，而对节点属性的利用较为有限——它们仅在初始层作为节点特征发挥作用。这种简单策略限制了节点属性在增强节点连接方面的潜力，导致无邻居或极少邻居的非活跃节点的感受野受限。此外，尽管研究表明重建节点属性具有益处，多数GNN的训练目标（即重建网络结构）也未能涵盖节点属性。因此，将节点属性深度融入GNN核心组件（包括图卷积操作与训练目标）具有重要意义。然而，由于需要恰当的集成方式以维持GNN的原有优势，这一任务颇具挑战性。为弥补这一不足，本文提出协同图神经网络（COllaborative graph Neural Networks, CONN），一种专为属性网络嵌入定制的GNN架构。该模型通过以下方式提升性能：1）选择性扩散来自邻居节点及其关联属性类别的消息；2）通过互相关机制联合重建节点-节点与节点-属性类别的交互关系。在真实网络上的实验表明，CONN以显著优势超越了当前最先进的嵌入算法。