  Graph neural networks (GNNs) have drawn increasing attention in recent years
and achieved remarkable performance in many graph-based tasks, especially in
semi-supervised learning on graphs. However, most existing GNNs are based on
the message-passing paradigm to iteratively aggregate neighborhood information
in a single topology space. Despite their success, the expressive power of GNNs
is limited by some drawbacks, such as inflexibility of message source
expansion, negligence of node-level message output discrepancy, and restriction
of single message space. To address these drawbacks, we present a novel
message-passing paradigm, based on the properties of multi-step message source,
node-specific message output, and multi-space message interaction. To verify
its validity, we instantiate the new message-passing paradigm as a
Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step
attention mechanism to aggregate node-specific multi-step neighborhood
information adaptively. Our proposed DPGNN can capture the structural
neighborhood information and the feature-related information simultaneously for
graph representation learning. Experimental results on six benchmark datasets
with different topological structures demonstrate that our method outperforms
the latest state-of-the-art models, which proves the superiority and
versatility of our method. To our knowledge, we are the first to consider
node-specific message passing in the GNNs.
