AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facilitate convenient analytics and utilization of wireless big data, we introduce the concept of knowledge graphs into the field of mobile networks, giving rise to what we term as wireless data knowledge graphs (WDKGs). However, the heterogeneous and dynamic nature of communication networks renders manual WDKG construction both prohibitively costly and error-prone, presenting a fundamental challenge. In this context, we propose an unsupervised data-and-model driven graph structure learning (DMGSL) framework, aimed at automating WDKG refinement and updating. Tackling WDKG heterogeneity involves stratifying the network into homogeneous layers and refining it at a finer granularity. Furthermore, to capture WDKG dynamics effectively, we segment the network into static snapshots based on the coherence time and harness the power of recurrent neural networks to incorporate historical information. Extensive experiments conducted on the established WDKG demonstrate the superiority of the DMGSL over the baselines, particularly in terms of node classification accuracy.

翻译：随着未来移动通信网络日益复杂，网络运营商面临巨大压力，人工智能在电信行业中的作用愈发关键。尽管业界日益认同智能网络自动驾驶是解决问题的关键，但其高度依赖于从网络数据中提取的专家经验与知识。为促进无线大数据的便捷分析与利用，我们将知识图谱概念引入移动网络领域，提出了无线数据知识图谱（WDKG）。然而，通信网络的异构性与动态特性使得人工构建WDKG成本极高且易出错，这构成了根本性挑战。在此背景下，我们提出了一种无监督的数据与模型双驱动的图结构学习（DMGSL）框架，旨在实现WDKG的自动化优化与更新。为处理WDKG的异构性，我们将网络分层为同质化层级并以更细粒度进行优化。此外，为有效捕捉WDKG的动态特性，我们基于相干时间将网络分割为静态快照，并利用循环神经网络整合历史信息。在已建立的WDKG上进行的广泛实验表明，DMGSL在基线方法中表现优异，尤其在节点分类准确度方面具有显著优势。