arXiv cs.AI· Bohan Yoon, Hyejin Jang·· 12 小时前AI 评分22
GDPool：面向层次化图神经网络的自适应图相关池化
Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks
AI 导读
研究者提出 Graph-Dependent Pooling（GDPool），为每个图动态确定池化粒度，不再依赖预定义簇数或固定池化比例。该方法通过梯度对齐分析揭示逐层独立监督的池化目标会给共享参数发送冲突梯度，并用自注意力选取图特定的锚点节点、以软节点分配构建粗化图。在金属有机框架与分子图基准上，GDPool 在所有基准中取得最佳预测性能，模块度持续高于现有方法，且活跃簇数量随深度单调下降。
正文
Abstract:Graph Neural Networks are widely used to learn structural representations of relational data. Hierarchical graph pooling learns multi-level representations by progressively coarsening graphs; however, prior methods rely on predefined cluster numbers or fixed pooling ratios, limiting adaptability to individual graphs. We propose Graph-Dependent Pooling (GDPool), which dynamically determines pooling granularity for each graph. We analyze optimization conflicts among layer-wise objectives through gradient alignment, showing that independently supervised pooling levels can send conflicting gradients to shared parameters. GDPool uses self-attention to select graph-specific anchors representing structurally important nodes and constructs coarse graphs through soft node assignment. Experiments on metal-organic framework and molecular graph benchmarks show that GDPool consistently achieves the best predictive performance across all benchmarks. Evaluations using modularity and the effective number of clusters show that GDPool achieves consistently higher modularity than existing methods while the number of active clusters decreases monotonically with depth. Sensitivity analyses confirm stable behavior across hyperparameter and architectural choices.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.33623 [cs.AI] |
| (or arXiv:2610.33623v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33623 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hyejin Jang [view email]
[v1]
Sat, 24 Oct 2026 11:54:16 UTC (1,966 KB)
来源：arXiv cs.AI · arxiv.org