Graph Neural Networks (GNNs) address challenges unique to graph-structured data, unlike traditional neural networks, which are often designed for grid-like or sequential data structures. CNNs, for instance, excel with grid-structured inputs like images, where relationships are defined within fixed, local neighborhoods. However, graphs have irregular structures where nodes can connect variably and don’t follow a fixed spatial layout. This makes it difficult for CNNs to capture relationships accurately, as they rely on a uniform structure.