Max Planck Institute for Intelligent Systems, Cyber Valley, 72076, Tübingen, Germany.
Bosch Industry on Campus Lab, University of Tübingen, Tübingen, Germany.
Sci Rep. 2022 May 30;12(1):8992. doi: 10.1038/s41598-022-12730-3.
Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed. The sparsity of the network is exploited for implementing a more efficient algorithm. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction. We find that the algorithm accurately retrieves the overall node's preference in different scenarios, and we show that it can distinguish small subsets of nodes that behave differently than the majority. As a consequence, the model can recognize whether a network has an overall preferred interaction mechanism. This is relevant in situations where there is no clear "a priori" information about what structure explains the observed network datasets well. Our model allows practitioners to learn this automatically from the data.
社区发现和层次结构提取通常被认为是网络上的两个独立推断任务。在研究实际数据时只考虑其中之一可能过于简单化。在这项工作中，我们提出了一种基于社区和层次结构相互作用的生成模型。它假设每个节点在交互机制中有偏好，具有相同偏好的节点更有可能相互作用，而仍然允许异质相互作用。利用网络的稀疏性来实现更有效的算法。我们在合成和真实世界数据上展示了我们的方法，并将性能与两种用于社区检测和排名提取的标准方法进行了比较。我们发现该算法在不同场景下能够准确地获取节点的总体偏好，并且我们表明它可以区分与大多数节点行为不同的一小部分节点。因此，该模型可以识别网络是否具有整体首选交互机制。在没有关于哪种结构可以很好地解释观察到的网络数据集的明确“先验”信息的情况下，这一点很重要。我们的模型允许从业者从数据中自动学习。
