论文标题
vstreamdrls：动态图表学习与企业分布式视频流解决方案有关的自我注意力
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
论文作者
论文摘要
实时视频流已经成为全球多家企业的标准通信解决方案的中流台。为了有效地将高质量的实时视频内容流式传输到大量办公室，公司采用分布式视频流解决方案，这些解决方案依赖于基础不断发展的企业网络的先验知识。但是，这样的网络是高度复杂和动态的。因此，为了最佳地协调实时视频分发，必须准确预测观众之间的可用网络容量。在本文中，我们在加权和动态图上提出了一种图表表示技术，以预测网络容量，即查看者/节点之间的连接/链接的权重。我们提出了一种具有自发机制的图形神经网络体系结构Vstreamdrls，以捕获实时视频流动事件的图形结构的演变。 VStreamDRLS在实时视频流活动的持续时间内采用图形卷积网络（GCN）模型，并引入了一种自我注意的机制来发展GCN参数。这样一来，我们的模型着重于与图形演变相关的GCN权重，并因此生成节点表示。我们在两个现实世界数据集上的链接预测任务上评估了我们的建议方法，该数据集由Enterprise Live Video流媒体事件生成。每个活动的持续时间持续了一个小时。实验结果证明了与最先进的策略相比，Vstreamdrl的有效性。我们的评估数据集和实施可在https://github.com/stefanosantaris/vstreamdrls上公开获得
Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls
