Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2024

Abstract

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.

Keywords

Graph neural network, Geodesic distance function, Node feature augmentation, Node classification

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 30th KDD 2024 : Barcelona, Spain, August 25-29

First Page

49

Last Page

58

ISBN

9798400704901

Identifier

10.1145/3637528.3671858

Publisher

ACM Digital Library

City or Country

Barcelona, Spain

Additional URL

https://doi.org/10.1145/3637528.3671858

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