We generalize triadic closure, along with previous generalizations of triadic closure, under an intuitive umbrella generalization: the Subgraph-to-Subgraph Transition (SST). We present algorithms and code to model graph evolution in terms of collections of these SSTs. We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results that simultaneously match state of the art graph neural network performance.

中文翻译：

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联合子图到子图转换——为强大且可解释的图建模推广三元闭包

我们将三元闭包以及之前对三元闭包的概括概括为一个直观的概括：子图到子图转换（SST）。我们提出了算法和代码，以根据这些 SST 的集合对图演化进行建模。然后我们使用 SST 框架为静态和时间、有向和无向图创建链接预测模型，这些模型产生高度可解释的结果，同时匹配最先进的图神经网络性能。