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arXiv · 2305.19125

Graph Generation with $K^2$-trees

Yunhui Jang·Dongwoo Kim·Sungsoo Ahn
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Abstract

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, originally designed for lossless graph compression. The $K^2$-tree representation {encompasses inherent hierarchy while enabling compact graph generation}. In addition, we make contributions by (1) presenting a sequential $K^2$-treerepresentation 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.

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Yunhui Jang, Dongwoo Kim, Sungsoo Ahn. 2023-05-30. Graph Generation with $K^2$-trees. https://arxiv.org/abs/2305.19125

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