ConstellationNet: Reinventing Spatial Clustering through GNNs
This addresses a bottleneck in spatial clustering for fields like criminology and urban planning, though it appears incremental as it combines existing CNN and GNN methods.
The paper tackled the problem of spatial clustering algorithms struggling with high-dimensional and large datasets by developing ConstellationNet, a CNN-GNN framework that achieved state-of-the-art performance in supervised classification and unsupervised clustering, reducing model size and training time by up to tenfold and improving baselines by 10 times.
Spatial clustering is a crucial field, finding universal use across criminology, pathology, and urban planning. However, most spatial clustering algorithms cannot pull information from nearby nodes and suffer performance drops when dealing with higher dimensionality and large datasets, making them suboptimal for large-scale and high-dimensional clustering. Due to modern data growing in size and dimension, clustering algorithms become weaker when addressing multifaceted issues. To improve upon this, we develop ConstellationNet, a convolution neural network(CNN)-graph neural network(GNN) framework that leverages the embedding power of a CNN, the neighbor aggregation of a GNN, and a neural network's ability to deal with batched data to improve spatial clustering and classification with graph augmented predictions. ConstellationNet achieves state-of-the-art performance on both supervised classification and unsupervised clustering across several datasets, outperforming state-of-the-art classification and clustering while reducing model size and training time by up to tenfold and improving baselines by 10 times. Because of its fast training and powerful nature, ConstellationNet holds promise in fields like epidemiology and medical imaging, able to quickly train on new data to develop robust responses.