Kolmogorov-Arnold Networks (KANs) Are Being Used To Boost Graph Deep Learning Like Never Before

A deep dive into how Graph Kolmogorov-Arnold Networks (GKANs) are improving Graph Deep Learning to surpass traditional approaches

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KANs have gained a lot of attention since they were published in April 2024.

They are being used to solve several machine-learning problems that previously used Multi-layer Perceptrons (MLPs), and their results have been impressive.

A team of researchers recently used KANs on Graph-structured data.

They called this new neural network architecture — Graph Kolmogorov-Arnold Networks (GKANs).

And, how did it go — you’d ask?

They found that GKANs achieve higher accuracy in semi-supervised learning tasks on a real-world graph dataset (Cora) than the traditional ML models used for Graph Deep Learning, i.e. Graph Convolutional Networks (GCNs).

This is a big step for KANs!

Here is a story where we dive deep into GKANs, learn how they are used with graph-structured data, and discuss how they surpass traditional approaches in Graph Deep Learning.