Graph Principal Flow Network for Conditional Graph Generation

Zhanfeng Mo, Tianze Luo, Sinno Jialin Pan

Keywords: graph generation, GFlowNets, generative flow networks

Abstract: Conditional graph generation is crucial and challenging since the conditional distribution of graph topology and feature is complicated and the semantic feature is hard to be captured by the generative model. In this work, we propose a novel graph conditional generative model, termed Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate graphs from low- to high-frequency components. Our GPrinFlowNet effectively captures the subtle yet essential semantic features of graph topology, resulting in high-quality generated graph data given a required condition. Extensive experiments and ablation studies showcase that our model achieves state-of-the-art performance compared to existing conditional graph generation models.

Track: Graph Algorithms and Learning for the Web

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Submission Number: 1535