  Graph neural networks (GNNs) have been proven to be effective in various
network-related tasks. Most existing GNNs usually exploit the low-frequency
signals of node features, which gives rise to one fundamental question: is the
low-frequency information all we need in the real world applications? In this
paper, we first present an experimental investigation assessing the roles of
low-frequency and high-frequency signals, where the results clearly show that
exploring low-frequency signal only is distant from learning an effective node
representation in different scenarios. How can we adaptively learn more
information beyond low-frequency information in GNNs? A well-informed answer
can help GNNs enhance the adaptability. We tackle this challenge and propose a
novel Frequency Adaptation Graph Convolutional Networks (FAGCN) with a
self-gating mechanism, which can adaptively integrate different signals in the
process of message passing. For a deeper understanding, we theoretically
analyze the roles of low-frequency signals and high-frequency signals on
learning node representations, which further explains why FAGCN can perform
well on different types of networks. Extensive experiments on six real-world
networks validate that FAGCN not only alleviates the over-smoothing problem,
but also has advantages over the state-of-the-arts.
