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.

中文翻译：

---

大规模时态网络特征提取和聚类的联合学习


时间网络在自然界和社会中无处不在，跟踪网络的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当前快照的拓扑（聚类精度）和历史快照的拓扑（聚类漂移）。当前的算法因无法在顶点级别表征网络的动态、特征提取和聚类的独立性以及高时间复杂度而受到批评。在本研究中，我们通过结合特征提取和聚类，提出了一种新颖的联合学习模型，用于时间网络中的动态社区检测（也称为 jLMDC），从而解决了这些问题。该模型被表述为约束优化问题。通过探索时间网络的拓扑结构，以充分利用其在每个时间步的动态，将顶点分为动态组和静态组。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的优点是在聚类的指导下提取特征，提升性能，节省算法的运行时间。最后，我们扩展 jLMDC 来检测时间网络中重叠的动态社区。在 11 个时间网络上的实验结果表明，与最先进的方法相比，jLMDC 的准确率提高了 8.23%，平均节省了 24.89% 的运行时间。