Encoder Embedding for General Graph and Node Classification

• URL: http://arxiv.org/abs/2405.15473v2
• Date: Tue, 22 Oct 2024 22:48:15 GMT
• Title: Encoder Embedding for General Graph and Node Classification
• Authors: Cencheng Shen,
• Abstract summary: We prove that the encoder embedding matrices satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves normality on a per-class basis, enabling optimal classification through discriminant analysis.
• Score: 4.178980693837599
• License:
• Abstract: 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.

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