This paper addresses the GNNs scalability issue by following a step-by-step approach, exploiting networking concepts to improve a baseline model.
Apr 25, 2022 · This paper addresses the GNNs scalability issue by following a step-by-step approach, exploiting networking concepts to improve a baseline model ...
This paper addresses the GNNs scalability issue by following a step-by-step approach, exploiting networking concepts to improve a baseline model. This work is ...
The main goal was to train the model with a dataset with network topologies of small size and then run the validation/testing phase over a dataset with larger ...
Predicting network performance using GNNs: generalization to larger unseen networks. dc.contributor.author, Farreras, Miquel. dc.contributor.author, Soto, Paola.
Jul 20, 2023 · This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called ...
The basic learning diagram of existing GNNs is to learn the parameters of GNNs from the training graphs, and then make predictions on unseen testing graphs.
The GNN has shown great potential in accurately predicting network properties, but existing solutions have a limitation in generalizing to larger networks.
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Nov 29, 2022 · high-accuracy results when predicting network KPIs in larger unseen networks, exhibiting good scalability properties. This paper describes ...
Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation. 1 ...