  Knowledge graphs (KGs) facilitate a wide variety of applications. Despite
great efforts in creation and maintenance, even the largest KGs are far from
complete. Hence, KG completion (KGC) has become one of the most crucial tasks
for KG research. Recently, considerable literature in this space has centered
around the use of Message Passing (Graph) Neural Networks (MPNNs), to learn
powerful embeddings. The success of these methods is naturally attributed to
the use of MPNNs over simpler multi-layer perceptron (MLP) models, given their
additional message passing (MP) component. In this work, we find that
surprisingly, simple MLP models are able to achieve comparable performance to
MPNNs, suggesting that MP may not be as crucial as previously believed. With
further exploration, we show careful scoring function and loss function design
has a much stronger influence on KGC model performance. This suggests a
conflation of scoring function design, loss function design, and MP in prior
work, with promising insights regarding the scalability of state-of-the-art KGC
methods today, as well as careful attention to more suitable MP designs for KGC
tasks tomorrow. Our codes are publicly available at:
https://github.com/Juanhui28/Are_MPNNs_helpful.
