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Dynamic graph representation learning strategies are based on differentneural architectures to capture the graph evolution over time. However, theunderlying neural architectures require a large amount of parameters to trainand suffer from high online inference latency, that is several model parametershave to be updated when new data arrive online. In this study we proposeDistill2Vec, a knowledge distillation strategy to train a compact model with alow number of trainable parameters, so as to reduce the latency of onlineinference and maintain the model accuracy high. We design a distillation lossfunction based on Kullback-Leibler divergence to transfer the acquiredknowledge from a teacher model trained on offline data, to a small-size studentmodel for online data. Our experiments with publicly available datasets showthe superiority of our proposed model over several state-of-the-art approacheswith relative gains up to 5% in the link prediction task. In addition, wedemonstrate the effectiveness of our knowledge distillation strategy, in termsof number of required parameters, where Distill2Vec achieves a compressionratio up to 7:100 when compared with baseline approaches. For reproductionpurposes, our implementation is publicly available athttps://stefanosantaris.github.io/Distill2Vec.

Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation