IV-GNN : Interval Valued Data Handling Using Graph Neural Network
This work addresses a specific limitation in graph neural networks for researchers and practitioners dealing with uncertain or imprecise data, representing an incremental advancement by extending feature spaces to intervals.
The paper tackles the problem of handling interval-valued node features in graph neural networks, which existing models cannot process, and demonstrates that the proposed IV-GNN model achieves competitive performance on benchmark datasets for graph classification tasks.
Graph Neural Network (GNN) is a powerful tool to perform standard machine learning on graphs. To have a Euclidean representation of every node in the Non-Euclidean graph-like data, GNN follows neighbourhood aggregation and combination of information recursively along the edges of the graph. Despite having many GNN variants in the literature, no model can deal with graphs having nodes with interval-valued features. This article proposes an Interval-ValuedGraph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable. Our model is much more general than existing models as any countable set is always a subset of the universal set $R^{n}$, which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification tasks by comparing its performance with those of the state-of-the-art models on several benchmark network and synthetic datasets.