HiGen: Hierarchical Graph Generative Networks
Keywords: Generative Model, Graph Neural Network, Hierarchical Model, Deep Neural Network
TL;DR: To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion.
Abstract: Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods.
In this work, we introduce *HiGen*, a **Hi**erarchical **G**raph G**en**erative Network
to address the limitations of existing generative models by incorporating community structures and cross-level interactions.
This approach involves generating graphs in a coarse-to-fine manner, where graph generation at each level is conditioned on a higher level (lower resolution) graph. The generation of communities at lower levels is performed in parallel, followed by the prediction of cross-edges between communities using a separate model. This parallelized approach enables high scalability.
To capture hierarchical relations, our model allows each node at a given level to depend not only on its neighbouring nodes but also on its corresponding super-node at the higher level.
Furthermore, we address the generation of integer-valued edge weights of the hierarchical structure by modeling the output distribution of edges using a multinomial distribution.
We show that multinomial distribution can be factorized successively, enabling the autoregressive generation of each community.
This property makes the proposed architecture well-suited for generating graphs with integer-valued edge weights.
Furthermore, by breaking down the graph generation process into the generation of multiple small partitions that are conditionally independent of each other, HiGen reduces its sensitivity to a predefined initial ordering of nodes.
Empirical studies demonstrate that the proposed generative model captures both local and global properties of graphs and achieves state-of-the-art performance in terms of graph quality on various benchmark graph datasets.
Submission Number: 57
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