Title: Causal Models for Growing Networks Show Chinese title

Title: 因果模型用于生长网络

Abstract: Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edges, we propose that the relevant symmetries refer to the \textit{causal structure} between them. We first enumerate the 96 causal directed acyclic graph (DAG) models over pairs of nodes (dyad variables) in a growing network with finite ancestral sets that are invariant to node deletion. We then partition them into 21 classes with ancestral sets that are closed under node marginalization. Several of these classes are remarkably amenable to distributed and asynchronous evaluation. As an example, we highlight a simple model that exhibits flexible power-law degree distributions and emergent phase transitions in sparsity, which we characterize analytically. With few parameters and much conditional independence, our proposed framework provides natural baseline models for causal inference in relational data. Show Chinese abstract

Abstract: 真实世界中的网络会随着时间推移而增长；基于节点可交换性的统计模型并不合适。 我们提议，相关的对称性不应限制边的 \textit{分布} 结构，而是应关注它们之间的 \textit{因果结构} 关系。 首先，我们在一个具有有限祖先集且对节点删除不变的生长网络中，枚举了节点对（二元变量）上的 96 种因果有向无环图（DAG）模型。 然后，我们将这些模型划分为 21 类，其中祖先集在节点边际化下闭合。 其中若干类模型非常适合分布式和异步评估。 作为示例，我们强调了一个简单的模型，该模型展示了灵活的幂律度分布以及稀疏性中的涌现相变现象，并对其进行了解析表征。 凭借少量参数和大量条件独立性，我们提出的框架为关系数据中的因果推理提供了自然的基本模型。