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 thanks to its highly expressive capability via message passing. Our study is motivated by the fact that  GNNs cannot be adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN, a new framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised inductive learning. Specifically, we utilize a k-nearest neighbor graph (kNNG) based on the similarity of node attributes to replace the GNN's computation graph defined by the neighborhood-based aggregation of each node. The known network structure is used to train model parameters, whereas a loss function is established  in such a way that our model learns the network structure. For the edgeless nodes, we inductively infer embeddings  by using edges via kNNG construction as a computation graph. By evaluating the performance of various downstream machine learning (ML) tasks, we empirically demonstrate that Edgeless-GNN consistently outperforms state-of-the-art methods of inductive network embedding  . Our framework is GNN-model-agnostic; thus, GNN models can be appropriately chosen according to ones' needs and ML tasks.
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 thanks to its highly expressive capability via message passing. Our study is motivated by the fact that existing GNNs cannot be adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN, a new framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised inductive learning. Specifically, we start by constructing a k-nearest neighbor graph (kNNG) based on the similarity of node attributes to replace the GNN's computation graph defined by the neighborhood-based aggregation of each node. As our main contributions, the known network structure is used to train model parameters, while a new loss function is established using energy-based learning in such a way that our model learns the network structure. For the edgeless nodes, we inductively infer embeddings for the edgeless nodes by using edges via kNNG construction as a computation graph. By evaluating the performance of various downstream machine learning (ML) tasks, we empirically demonstrate that Edgeless-GNN consistently outperforms state-of-the-art methods of inductive network embedding . Moreover, our findings corroborate the effectiveness of Edgeless-GNN in judiciously combining the replaced computation graph with our newly designed loss . Our framework is GNN-model-agnostic; thus, GNN models can be appropriately chosen according to ones' needs and ML tasks.
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