Undisciplined Over Smoothing

Undisciplined over-smoothing in graph neural networks (GNNs) refers to the phenomenon where, as network depth increases, node representations become increasingly similar, hindering performance. Current research focuses on mitigating this issue through various techniques, including modified aggregation functions, architectural innovations like residual connections and skip connections, and the development of novel normalization methods. Addressing over-smoothing is crucial for realizing the full potential of deep GNNs across diverse applications, from graph classification to natural language processing and image analysis, enabling more accurate and robust models for complex data.