Dynamic Graph Representation Learning on Enterprise Live Video Streaming Events

Abstract: Enterprises use live video streaming as a mean of communication. Streaming high-quality video to thousands of devices in a corporate network is not an easy task; the bandwidth requirements often exceed the network capacity. For that matter, Peer-To-Peer (P2P) networks have been proven beneficial, as peers can exchange content efficiently by utilizing the topology of the corporate network. However, such networks are dynamic and their topology might not always be known. In this project we propose ABD, a new dynamic graph representation learning approach, which aims to estimate the bandwidth capacity between peers in a corporate network. The architecture of ABDis adapted to the properties of corporate networks. The model is composed of an attention mechanism and a decoder. The attention mechanism produces node embeddings, while the decoder converts those embeddings into bandwidth predictions. The model aims to capture both the dynamicity and the structure of the dynamic network, using an advanced training process. The performance of ABD is tested with two dynamic graphs which were produced by real corporate networks. Our results show that ABD achieves better results when compared to existing state-of-the-art dynamic graph representation learning models.