Graph Generation with $K^2$-trees

arXiv:2305.19125v4 Announce Type: replace
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 …

abstract analysis arxiv challenge compression cs.ai cs.lg cs.si discovery distribution domains drug discovery enabling graph graphs network novel representation social tree trees type work