  We propose a novel solution to addressing a long-standing dilemma in the
representation learning of graph neural networks (GNNs): how to effectively
capture and represent useful information embedded in long-distance nodes to
improve the performance of nodes with low homophily without leading to
performance degradation in nodes with high homophily. This dilemma limits the
generalization capability of existing GNNs. Intuitively, interactions with
distant nodes introduce more noise for a node than those with close neighbors.
However, in most existing works, messages being passed among nodes are mingled
together, which is inefficient from a communication perspective. Our solution
is based on a novel, simple, yet effective aggregation scheme, resulting in a
ladder-style GNN architecture, namely LADDER-GNN. Specifically, we separate
messages from different hops, assign different dimensions for them, and then
concatenate them to obtain node representations. Such disentangled
representations facilitate improving the information-to-noise ratio of messages
passed from different hops. To explore an effective hop-dimension relationship,
we develop a conditionally progressive neural architecture search strategy.
Based on the searching results, we further propose an efficient approximate
hop-dimension relation function to facilitate the rapid configuration of the
proposed LADDER-GNN. We verify the proposed LADDER-GNN on seven diverse
semi-supervised node classification datasets. Experimental results show that
our solution achieves better performance than most existing GNNs. We further
analyze our aggregation scheme with two commonly used GNN architectures, and
the results corroborate that our scheme outperforms existing schemes in
classifying low homophily nodes by a large margin.
