Kolmogorov-Arnold Graph Neural Networks Applied to Inorganic Nanomaterials Dataset
This work addresses a gap in applying KAGNNs to inorganic nanomaterials datasets, which is incremental as it extends existing methods to new data.
The authors tackled the problem of applying Kolmogorov-Arnold Graph Neural Networks (KAGNNs) to inorganic nanomaterials datasets, where they achieved state-of-the-art results, particularly on the CHILI-3K dataset, surpassing conventional GNNs in classification.
The recent development of Kolmogorov-Arnold Networks (KANs) introduced new discoveries in the field of Graph Neural Networks (GNNs), expanding the existing set of models with KAN-based versions of GNNs, which often surpass the accuracy of MultiLayer Perceptron (MLP)-based GNNs. These models were widely tested on the graph datasets consisting of organic molecules; however, those studies disregarded the inorganic nanomaterials datasets. In this work, we close this gap by applying Kolmogorov-Arnold Graph Neural Networks (KAGNNs) to a recently published large inorganic nanomaterials dataset called CHILI. For this, we adapt and test KAGNNs appropriate for this dataset. Our experiments reveal that on the CHILI datasets, particularly on the CHILI-3K, KAGNNs substantially surpass conventional GNNs in classification, achieving state-of-the-art results.