A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs
This work addresses node classification tasks in graph-based applications, representing an incremental improvement over existing methods.
The paper tackles the problem of node feature augmentation on graphs by introducing LGGD, a method that learns generalized geodesic distances to generate node features, resulting in improved node classification performance with competitive results against state-of-the-art methods on real-world datasets.
Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called `LGGD' (Learned Generalized Geodesic Distances). This method involves generating node features by learning a generalized geodesic distance function through a training pipeline that incorporates training data, graph topology and the node content features. The strength of this method lies in the proven robustness of the generalized geodesic distances to noise and outliers. Our contributions encompass improved performance in node classification tasks, competitive results with state-of-the-art methods on real-world graph datasets, the demonstration of the learnability of parameters within the generalized geodesic equation on graph, and dynamic inclusion of new labels.