  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 ($k$NNG) 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 $k$NNG 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.
