As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will greatly impact company revenue and user trust. Due to the cascading effect, cloud failure often comes with an overwhelming number of incidents from dependent services and devices. To pursue an efficient incident management, related incidents should be quickly aggregated to narrow down the problem scope. To this end, in this paper, we propose GRLIA, an incident aggregation framework based on graph representation learning over a graph of cascaded cloud failures. The graph representation is learned for each unique incident in an unsupervised and unified fashion to simultaneously encode the topological and temporal relationship among incidents. Therefore, it can be easily employed for online incident aggregation by measuring their distance. Furthermore, we leverage fine-grained system monitoring data, i.e., Key Performance Indicators (KPIs), to identify the complete scope of failures’ cascading impact. The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of company $\mathcal{H}$. The experimental results demonstrate that GRLIA is effective and outperforms existing methods. Furthermore, our framework has been successfully deployed in industrial practice.