We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of many important concepts generalizing classical graphs. This includes in particular size, density, clique, neighborhood, degree, clustering coefficient, and transitivity. In this contribution, we show that, like graphs, stream graphs may be extended to cope with bipartite structures, with node and link weights, or with link directions. We review the main bipartite, weighted or directed graph concepts proposed in the literature, we generalize them to the cases of bipartite, weighted, or directed stream graphs, and we show that obtained concepts are consistent with graph and stream graph ones. This provides a formal ground for an accurate modeling of the many temporal networks that have one or several of these features.

翻译：我们最近为时间网络建模引入了一种形式主义,我们称之为流图。它强调数据流的性质,并允许严格定义对古典图进行概括的许多重要概念。这包括特定的大小、密度、密类、邻里、度、集聚系数和中转性。在这个贡献中,我们显示,像图表一样,流图可以扩展,以应对双边结构,有节点和连接权重,或链接方向。我们审查了文献中提议的主要双边、加权或定向图表概念,我们把它们概括为双边、加权或定向的流图,我们表明获得的概念与图表和流图相一致。这为精确建模具有一个或多个这些特征的许多时间网络提供了一个正式的地面。