  Modern graph neural networks (GNNs) use a message passing scheme and have
achieved great success in many fields. However, this recursive design
inherently leads to excessive computation and memory requirements, making it
not applicable to massive real-world graphs. In this work, we propose the
Neighbor2Seq to transform the hierarchical neighborhood of each node into a
sequence. This novel transformation enables the subsequent mini-batch training
for general deep learning operations, such as convolution and attention, that
are designed for grid-like data and are shown to be powerful in various
domains. Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency
and advantages of deep learning operations on grid-like data by precomputing
the Neighbor2Seq transformations. We evaluate our method on a massive graph,
with more than 111 million nodes and 1.6 billion edges, as well as several
medium-scale graphs. Results show that our proposed method is scalable to
massive graphs and achieves superior performance across massive and
medium-scale graphs. Our code is available at
https://github.com/divelab/Neighbor2Seq.
