  In this study, we present a dynamic graph representation learning model on
weighted graphs to accurately predict the network capacity of connections
between viewers in a live video streaming event. We propose EGAD, a neural
network architecture to capture the graph evolution by introducing a
self-attention mechanism on the weights between consecutive graph convolutional
networks. In addition, we account for the fact that neural architectures
require a huge amount of parameters to train, thus increasing the online
inference latency and negatively influencing the user experience in a live
video streaming event. To address the problem of the high online inference of a
vast number of parameters, we propose a knowledge distillation strategy. In
particular, we design a distillation loss function, aiming to first pretrain a
teacher model on offline data, and then transfer the knowledge from the teacher
to a smaller student model with less parameters. We evaluate our proposed model
on the link prediction task on three real-world datasets, generated by live
video streaming events. The events lasted 80 minutes and each viewer exploited
the distribution solution provided by the company Hive Streaming AB. The
experiments demonstrate the effectiveness of the proposed model in terms of
link prediction accuracy and number of required parameters, when evaluated
against state-of-the-art approaches. In addition, we study the distillation
performance of the proposed model in terms of compression ratio for different
distillation strategies, where we show that the proposed model can achieve a
compression ratio up to 15:100, preserving high link prediction accuracy. For
reproduction purposes, our evaluation datasets and implementation are publicly
available at https://stefanosantaris.github.io/EGAD.
