Geometric graphs from data to aid classification tasks with graph convolutional networks
This work addresses classification tasks in scientific domains by enhancing performance through graph construction, though it is incremental as it builds on existing graph-based methods.
The paper tackled the problem of improving classification when relational information is not available by constructing geometric graphs from features and using them in Graph Convolutional Networks, resulting in increased classification accuracy with optimized low-density graphs and spectral sparsification.
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from various scientific domains.