论文标题
联合子图到纸透明过渡 - 概括三元闭合，以进行功能强大且可解释的图形建模
Joint Subgraph-to-Subgraph Transitions -- Generalizing Triadic Closure for Powerful and Interpretable Graph Modeling
论文作者
论文摘要
在直观的伞上，我们将三元闭合以及先前的三元闭合概括：亚图表到纸透透明（SST）中概括。我们提出算法和代码，以根据这些SST的集合来建模图演变。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，从而产生高度可解释的结果。从数量上讲，我们的模型符合最先进的图形神经网络模型的开箱即用性能，从而验证了我们可解释结果的正确性和有意义。
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. Quantitatively, our models match out-of-the-box performance of state of the art graph neural network models, thereby validating the correctness and meaningfulness of our interpretable results.
