Li Dongyuan, Ma Xiaoke, Gong Maoguo
IEEE Trans Cybern. 2023 Mar;53(3):1653-1666. doi: 10.1109/TCYB.2021.3107679. Epub 2023 Feb 15.
Temporal networks are ubiquitous in nature and society, and tracking the dynamics of networks is fundamental for investigating the mechanisms of systems. Dynamic communities in temporal networks simultaneously reflect the topology of the current snapshot (clustering accuracy) and historical ones (clustering drift). Current algorithms are criticized for their inability to characterize the dynamics of networks at the vertex level, independence of feature extraction and clustering, and high time complexity. In this study, we solve these problems by proposing a novel joint learning model for dynamic community detection in temporal networks (also known as jLMDC) via joining feature extraction and clustering. This model is formulated as a constrained optimization problem. Vertices are classified into dynamic and static groups by exploring the topological structure of temporal networks to fully exploit their dynamics at each time step. Then, jLMDC updates the features of dynamic vertices by preserving features of static ones during optimization. The advantage of jLMDC is that features are extracted under the guidance of clustering, promoting performance, and saving the running time of the algorithm. Finally, we extend jLMDC to detect the overlapping dynamic community in temporal networks. The experimental results on 11 temporal networks demonstrate that jLMDC improves accuracy up to 8.23% and saves 24.89% of running time on average compared to state-of-the-art methods.
时间网络在自然界和社会中无处不在，追踪网络动态是研究系统机制的基础。时间网络中的动态社区同时反映了当前快照的拓扑结构（聚类准确性）和历史快照的拓扑结构（聚类漂移）。当前的算法因无法在顶点级别刻画网络动态、特征提取与聚类相互独立以及时间复杂度高而受到批评。在本研究中，我们通过提出一种用于时间网络中动态社区检测的新型联合学习模型（也称为jLMDC）来解决这些问题，该模型通过将特征提取和聚类相结合。此模型被表述为一个约束优化问题。通过探索时间网络的拓扑结构，将顶点分为动态和静态组，以充分利用它们在每个时间步的动态特性。然后，jLMDC在优化过程中通过保留静态顶点的特征来更新动态顶点的特征。jLMDC的优势在于在聚类的指导下提取特征，提升了性能，并节省了算法的运行时间。最后，我们将jLMDC扩展用于检测时间网络中的重叠动态社区。在11个时间网络上的实验结果表明，与现有方法相比，jLMDC的准确率提高了8.23%，平均运行时间节省了24.89%。
