标题： 基于动态属性网络演化的社区解释 显示英文标题

标题： Interpreting communities based on the evolution of a dynamic attributed network

摘要： 许多方法已被提出用于检测社区，不仅在普通网络中，还在属性网络、有向网络甚至动态复杂网络中。 从建模的角度来看，为了具有一定的实用性，社区结构必须相对于所研究系统的特性来表征。 然而，现有的大多数工作都集中在社区的检测上，只有极少数尝试解决这一解释问题。 此外，现有的方法要么受到它们处理的数据类型的限制，要么受到它们输出结果性质的限制。 在本工作中，我们将社区的解释视为一个独立于检测过程的问题，即识别社区的最典型特征。 我们给出了这个问题的形式化定义，并提出了一种解决方法。 为此，我们首先定义了网络的基于序列的表示，结合时间信息、社区结构、拓扑度量和节点属性。 然后描述了如何识别该数据集中最突出的序列模式，并用它们来表征社区。 我们在人工生成的动态属性网络上研究了我们方法的性能。 我们还在现实世界的系统上对我们的框架进行了实证验证：一个科学合作的DBLP网络，以及一个社交和音乐互动的LastFM网络。 显示英文摘要

摘要： Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle, or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.