Encoder Embedding for General Graph and Node Classification
<|reference_start|>Encoder Embedding for General Graph and Node Classification: 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.<|reference_end|>
@article{shen2024encoder,
    title={Encoder Embedding for General Graph and Node Classification},
    author={Cencheng Shen},
    journal={Applied Network Science 9, 66, 2024},
    year={2024},
    doi={10.1007/s41109-024-00678-4},
    archivePrefix={arXiv},
    eprint={2405.15473},
    primaryClass={stat.ML cs.LG cs.SI}
}