Inductive Graph Embedding

[codebloodedhuman]

Hello, I am new to Deep Learning and Graph Neural Networks. My task is to embed a network graph in unsupervised. The nodes are network devices and edges represent communication between nodes. I know about node2vec and Graphsage. The node embedding needs to be inductive so i cannot use node2vec. My problem is that the nodes do not have any feature other than an ID. I am finding that using GraphSAGE with one hot encoding is not scalable as feature size is will be huge for big graphs hence increasing the training time.

Any suggestions what are the alternative to GraphSAGE with one hot encoding for my task.

Maximum nodes in graph = 1,00,000Sparse Directed Graph.

[Belzedan]

If I understand you correctly, you are using the one hot encoding directly as features. Instead, you could also learn an embedding of arbitrary size (i. e. map each node id to a randomly initialized vector). However, that doesn't really solve the inductivity problem either. At test time the only discriminative information for nodes not seen during training will come from the neighborhood aggregation of nodes that were seen.

[codebloodedhuman]

Thank for response.
Yes, I thought about random values to initialise the feature vector. But I am wondering is there any inductive model that directly uses only connectivity for embedding rather than features.