Encoder Embedding for General Graph and Node Classification
This work addresses the need for scalable vertex-level representations in general graph models, offering incremental improvements for applications like text and image analysis.
The paper extends graph encoder embedding to handle weighted graphs, distance matrices, and kernel matrices, proving theoretical properties like asymptotic normality and achieving optimal classification in experiments with text and image data.
Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves asymptotic normality on a per-class basis, enabling optimal classification through discriminant analysis. These theoretical findings are validated through a series of experiments involving weighted graphs, as well as text and image data transformed into general graph representations using appropriate distance metrics.