We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.

翻译：我们提出了一种用于动态网络的新型模型选择方法。该方法通过在大量合成网络数据上训练分类器实现，这些数据由模拟九种最先进的动态网络随机图模型生成，参数范围确保网络规模随时间呈指数增长。我们设计了一种概念新颖的动态特征类型，用于统计特定时间间隔内顶点组接收的新链接数量。所提出的特征易于计算、可解析处理且具备可解释性。该方法在合成网络分类上近乎完美，远超现有最优水平。将我们的分类方法应用于真实引文网络，验证了文献中关于优先连接、适应性和老化模型最符合真实引文网络特征的论断，尽管有时预测模型不包含顶点适应性参数。