An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard regression methods that make use of both types of covariates may be used for prediction, statistical inference is complicated by the fact that the nodal summary statistics are often dependent in complex ways. We show that under a mild joint exchangeability assumption, a network analog of conformal prediction achieves finite sample validity for a wide range of network covariates. We also show that a form of asymptotic conditional validity is achievable. The methods are illustrated on both simulated networks and a citation network dataset.

翻译：网络分析的一个重要问题是利用两个网络变量预测一个节点属性,例如图形嵌入坐标或地方子绘图计数,以及传统的节点共变数,例如人口特征。虽然使用两种类型的共变数的标准回归方法可用于预测,但由于节点摘要统计数据往往以复杂的方式依赖。我们表明,在一种温和的共同互换假设下,一个符合预测的网络类比为一系列网络变量取得了有限的样本有效性。我们还表明,一种无症状的有条件有效性是可以实现的。这些方法在模拟网络和引用网络数据集上都作了说明。