The content introduces a novel method, Hierarchical Graph Generation with K2–Trees (HGGT), that utilizes K2–tree representation for graph generation. It discusses the challenges in generating graphs and the significance of deep generative models. The K2–tree representation is explained in detail, emphasizing its hierarchical and compact structure. The sequential K2–tree representation and its autoregressive generation process are outlined. The content also covers related work, lossless graph compression, and the algorithm for constructing a K2–tree from a graph. The proposed method is validated through empirical results on various graph datasets.
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by Yunhui Jang,... alle arxiv.org 03-27-2024
https://arxiv.org/pdf/2305.19125.pdfDomande più approfondite