  In recent years, Graph Neural Networks (GNNs) have shown superior performance
on diverse real-world applications. To improve the model capacity, besides
designing aggregation operations, GNN topology design is also very important.
In general, there are two mainstream GNN topology design manners. The first one
is to stack aggregation operations to obtain the higher-level features but
easily got performance drop as the network goes deeper. Secondly, the multiple
aggregation operations are utilized in each layer which provides adequate and
independent feature extraction stage on local neighbors while are costly to
obtain the higher-level information. To enjoy the benefits while alleviating
the corresponding deficiencies of these two manners, we learn to design the
topology of GNNs in a novel feature fusion perspective which is dubbed
F$^2$GNN. To be specific, we provide a feature fusion perspective in designing
GNN topology and propose a novel framework to unify the existing topology
designs with feature selection and fusion strategies. Then we develop a neural
architecture search method on top of the unified framework which contains a set
of selection and fusion operations in the search space and an improved
differentiable search algorithm. The performance gains on eight real-world
datasets demonstrate the effectiveness of F$^2$GNN. We further conduct
experiments to show that F$^2$GNN can improve the model capacity while
alleviating the deficiencies of existing GNN topology design manners,
especially alleviating the over-smoothing problem, by utilizing different
levels of features adaptively.
