  Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de
facto methods to extract spatial-temporal information for temporal graph
learning. Interestingly, we found that although both RNN and SAM could lead to
a good performance, in practice neither of them is always necessary. In this
paper, we propose GraphMixer, a conceptually and technically simple
architecture that consists of three components: (1) a link-encoder that is only
based on multi-layer perceptrons (MLP) to summarize the information from
temporal links, (2) a node-encoder that is only based on neighbor mean-pooling
to summarize node information, and (3) an MLP-based link classifier that
performs link prediction based on the outputs of the encoders. Despite its
simplicity, GraphMixer attains an outstanding performance on temporal link
prediction benchmarks with faster convergence and better generalization
performance. These results motivate us to rethink the importance of simpler
model architecture.
