  Graph neural networks (GNNs) have shown great success in learning from
graph-based data. The key mechanism of current GNNs is message passing, where a
node's feature is updated based on the information passing from its local
neighbourhood. A limitation of this mechanism is that node features become
increasingly dominated by the information aggregated from the neighbourhood as
we use more rounds of message passing. Consequently, as the GNN layers become
deeper, adjacent node features tends to be similar, making it more difficult
for GNNs to distinguish adjacent nodes, thereby, limiting the performance of
GNNs. In this paper, we propose conditional local feature encoding (CLFE) to
help prevent the problem of node features being dominated by the information
from local neighbourhood. The idea of our method is to extract the node hidden
state embedding from message passing process and concatenate it with the nodes
feature from previous stage, then we utilise linear transformation to form a
CLFE based on the concatenated vector. The CLFE will form the layer output to
better preserve node-specific information, thus help to improve the performance
of GNN models. To verify the feasibility of our method, we conducted extensive
experiments on seven benchmark datasets for four graph domain tasks:
super-pixel graph classification, node classification, link prediction, and
graph regression. The experimental results consistently demonstrate that our
method improves model performance across a variety of baseline GNN models for
all four tasks.
