Graph Data Augmentation via Contrastive Generator Inversion (textttDCBA)

Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based machine learning methods is often constrained by the limited availability of large and diverse graph datasets. In this paper, we introduce textttDCBA, a model-based approach to graph data augmentation that infers the configuration of a synthetic graph generator from an observed network. We instantiate the proposed framework using the textttABCD generator, which produces scale-free networks with community structure. Our model learns a joint representation of graphs and generator parametrisations using a multi-positive contrastive objective with soft negative weighting. The learned representation enables the prediction of an textttABCD configuration whose stochastic realisations preserve the macrostructural properties encoded by the generator. Experiments show that textttDCBA recovers generator parameters more accurately and robustly than an algorithmic inverse-modelling baseline. Its downstream utility is further demonstrated in community detection, where inferred configurations used to fine-tune textttPRoCD improve AMI on average by 161% on synthetic and 273% on real-world networks.