Generating graphs from a target distribution is a significant challenge
across many domains, including drug discovery and social network analysis. In
this work, we introduce a novel graph generation method leveraging $K^2$-tree
representation which was originally designed for lossless graph compression.
Our motivation stems from the ability of the $K^2$-trees to enable compact
generation while concurrently capturing the inherent hierarchical structure of
a graph. In addition, we make further contributions by (1) presenting a
sequential $K^2$-tree representation that incorporates pruning, flattening, and
tokenization processes and (2) introducing a Transformer-based architecture
designed to generate the sequence by incorporating a specialized tree
positional encoding scheme. Finally, we extensively evaluate our algorithm on
four general and two molecular graph datasets to confirm its superiority for
graph generation.

本文提出了一种使用 K²-tree 表示生成图表的新方法，并通过引入分级结构和去枝、展开及分词过程的顺序 K²-tree 表示以及使用专用树位置编码方案的 Transformer 架构来进一步完善。在四个常规图形和两个分子图形的数据集上进行广泛评估后，证实了我们的算法在图形生成方面的优越性。