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GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generation
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AI
DOI：10.1007/978-3-032-07904-6_10.png)
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Abstract
En 中文
Brain connectomes, which represent neural connectivity as graphs, are crucial for understanding brain organization but are costly and time-consuming to acquire, motivating the development of generative methods. Recent progress in graph generative modeling offers a convenient data-driven alternative, enabling synthetic generation of connectomes and alleviating the dependence on extensive neuroimaging acquisitions. However, existing generative models still have the following key limitations: (i) they compress the entire graph into a single latent code, such as variational graph autoencoders (VGAEs), thereby blurring fine-grained local motifs; (ii) they rely on rich node attributes that brain connectomes typically do not provide, which diminishes reconstruction quality; (iii) edge-centric models like dual-graph frameworks emphasize topology but often overlook the accurate prediction of edge weights, sacrificing quantitative fidelity; and (iv) many state-of-the-art architectures, such as those using edge-conditioned convolutions, employ computationally expensive architectures, which results in significant memory demands and limits their scalability to larger connectomes. To address these limitations, we propose GraphTreeGen (GTG), a novel subtree-centric generative framework explicitly designed for efficient and accurate brain connectome generation. GTG decomposes each connectome into entropy-guided k-hop trees that capture informative local structure, which are then encoded by a shared GCN. A bipartite message-passing layer merges subtree embeddings with global node features, and a dual-branch decoder jointly predicts edge existence and weights to rebuild the full adjacency matrix. Experiments demonstrate that GTG significantly outperforms state-of-the-art baselines in self-supervised task, and remains competitive in supervised settings, delivering higher structural fidelity and more precise edge weights while using far less memory. Its modular, resource-efficient design also lays the groundwork for extensions to connectome super-resolution and cross-modality synthesis.
Keywords:
Brain Connectome Generation
Tree-Based Graph Generation
Memory-Efficient GNNs
Computational Trees
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