We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose $\mathsf{Edgeless-GNN}$ , a novel inductive framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised learning . Specifically, we start by constructing a proxy graph based on the similarity of node attributes as the GNN's computation graph defined by the underlying network. The known network structure is used to train model parameters, whereas a topology-aware loss function is established such that our model judiciously learns the network structure by encoding positive, negative, and second-order relations between nodes. For the edgeless nodes, we inductively infer embeddings by expanding the computation graph. By evaluating the performance of various downstream machine learning tasks, we empirically demonstrate that $\mathsf{Edgeless-GNN}$ exhibits (a) superiority over state-of-the-art inductive network embedding methods for edgeless nodes, (b) effectiveness of our topology-aware loss function, (c) robustness to incomplete node attributes, and (d) a linear scaling with the graph size.

中文翻译：

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Edgeless-GNN：无边缘节点的无监督表示学习

我们研究嵌入无边缘节点的问题，例如新进入底层网络的用户，同时使用广泛研究的图神经网络（GNN）来进行图的有效表示学习。我们研究的动机是 GNN 不能直接用于我们的问题，因为消息传递到这种没有连接的无边缘节点是不可能的。为了应对这一挑战，我们建议$\mathsf{无边缘-GNN}$ ，一种新颖的归纳框架，使 GNN 能够通过无监督学习生成节点嵌入，甚至可以为无边缘节点生成节点嵌入 。具体来说，我们首先根据节点属性的相似性构建一个代理图作为底层网络定义的 GNN 计算图。已知的网络结构用于训练模型参数，而建立拓扑感知损失函数，使我们的模型通过编码节点之间的正、负和二阶关系来明智地学习网络结构。对于无边节点，我们通过扩展计算图来归纳推断嵌入。通过评估各种下游机器学习任务的性能，我们凭经验证明：$\mathsf{无边缘-GNN}$表现出（a）优于最先进的无边缘节点归纳网络嵌入方法，（b）我们的拓扑感知损失函数的有效性，（c）对不完整节点属性的鲁棒性，以及（d）线性缩放图形大小。