对比图模型与神经网络如何处理网络数据，发现图模型在噪声和异质性下更稳健。
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
- 用链接预测任务比较图模型与图神经网络的机制差异
- 低维或带噪特征下，图模型性能优于图神经网络
- 图模型在高异质性网络中更鲁棒，且解释性更强
图是表示关系数据的强大结构，广泛用于描述复杂现实系统。概率图模型（PGMs）与图神经网络（GNNs）均可利用图结构数据，但其内在机制不同。本文通过链接预测任务，在合成与真实网络上开展三项实验：一是分析两者对节点特征的依赖程度；二是评估其对噪声特征的鲁棒性；三是研究异质性增加时的表现。结果表明，PGMs 不依赖节点特征，而 GNNs 无法仅靠边信息建模；当特征为低维或含噪时，GNNs 表现逊于 PGMs，符合多数真实场景；随着图异质性增强，PGMs 的鲁棒性优于 GNNs。此外，还从计算复杂度与可解释性角度对比两者表现。
原文摘要 · Abstract (English)
Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question is how do they compare in capturing the information contained in networked datasets? We address this objective by solving a link prediction task and we conduct three main experiments, on both synthetic and real networks: one focuses on how PGMs and GNNs handle input features, while the other two investigate their robustness to noisy features and increasing heterophily of the graph. PGMs do not necessarily require features on nodes, while GNNs cannot exploit the network edges alone, and the choice of input features matters. We find that GNNs are outperformed by PGMs when input features are low-dimensional or noisy, mimicking many real scenarios where node attributes might be scalar or noisy. Then, we find that PGMs are more robust than GNNs when the heterophily of the graph is increased. Finally, to assess performance beyond prediction tasks, we also compare the two frameworks in terms of their computational complexity and interpretability.
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