Identifying Network Congestion Using Knowledge Graphs and Link Prediction
Katerina Mitropoulou, Panagiotis C. Kokkinos, Emmanouel Varvarigos · 2023
In this work, we introduce a dynamic, context-aware system for managing communication networks using knowledge graphs. We utilize graph embeddings to create vector representations of a network's properties, while preserving each node's topological data. Our model employs link prediction techniques to proactively identify potential network congestion events. This methodology has been applied in a simulated communication network environment. The results demonstrate its promising ability to enhance network performance and reliability by predicting and mitigating congestion before it disrupts service delivery. By leveraging this enriched representation, our model identifies events that could disturb efficient network function, hence enabling more efficient and reliable delivery of digital services. This approach significantly contributes to the proactive and predictive management of digital communication networks, establishing a new way of enhancing network performance and reliability.