  Message passing has become the dominant framework in graph representation
learning. The essential idea of the message-passing framework is to update node
embeddings based on the information aggregated from local neighbours. However,
most existing aggregation methods have not encoded neighbour-level message
interactions into the aggregated message, resulting in an information lost in
embedding generation. And this information lost could be accumulated and become
more serious as more layers are added to the graph network model. To address
this issue, we propose a neighbour-level message interaction information
encoding method for improving graph representation learning. For messages that
are aggregated at a node, we explicitly generate an encoding between each
message and the rest messages using an encoding function. Then we aggregate
these learned encodings and take the sum of the aggregated encoding and the
aggregated message to update the embedding for the node. By this way,
neighbour-level message interaction information is integrated into the
generated node embeddings. The proposed encoding method is a generic method
which can be integrated into message-passing graph convolutional networks.
Extensive experiments are conducted on six popular benchmark datasets across
four highly-demanded tasks. The results show that integrating neighbour-level
message interactions achieves improved performance of the base models,
advancing the state of the art results for representation learning over graphs.
