  Graph neural networks (GNNs) have achieved tremendous success on multiple
graph-based learning tasks by fusing network structure and node features.
Modern GNN models are built upon iterative aggregation of neighbor's/proximity
features by message passing. Its prediction performance has been shown to be
strongly bounded by assortative mixing in the graph, a key property wherein
nodes with similar attributes mix/connect with each other. We observe that real
world networks exhibit heterogeneous or diverse mixing patterns and the
conventional global measurement of assortativity, such as global assortativity
coefficient, may not be a representative statistic in quantifying this mixing.
We adopt a generalized concept, node-level assortativity, one that is based at
the node level to better represent the diverse patterns and accurately quantify
the learnability of GNNs. We find that the prediction performance of a wide
range of GNN models is highly correlated with the node level assortativity. To
break this limit, in this work, we focus on transforming the input graph into a
computation graph which contains both proximity and structural information as
distinct type of edges. The resulted multi-relational graph has an enhanced
level of assortativity and, more importantly, preserves rich information from
the original graph. We then propose to run GNNs on this computation graph and
show that adaptively choosing between structure and proximity leads to improved
performance under diverse mixing. Empirically, we show the benefits of adopting
our transformation framework for semi-supervised node classification task on a
variety of real world graph learning benchmarks.
