Effect of Choosing Loss Function when Using T-batching for
  Representation Learning on Dynamic Networks
Erfan Loghmani MohammadAmin Fazli
Representation learning methods have revolutionized machine learning on
networks by converting discrete network structures into continuous domains.
However, dynamic networks that evolve over time pose new challenges. To address
this, dynamic representation learning methods have gained attention, offering
benefits like reduced learning time and improved accuracy by utilizing temporal
information.
  T-batching is a valuable technique for training dynamic network models that
reduces training time while preserving vital conditions for accurate modeling.
However, we have identified a limitation in the training loss function used
with t-batching. Through mathematical analysis, we propose two alternative loss
functions that overcome these issues, resulting in enhanced training
performance.
  We extensively evaluate the proposed loss functions on synthetic and
real-world dynamic networks. The results consistently demonstrate superior
performance compared to the original loss function. Notably, in a real-world
network characterized by diverse user interaction histories, the proposed loss
functions achieved more than 26.9% enhancement in Mean Reciprocal Rank (MRR)
and more than 11.8% improvement in Recall@10. These findings underscore the
efficacy of the proposed loss functions in dynamic network modeling.