  Graph neural networks are emerging as continuation of deep learning success
w.r.t. graph data. Tens of different graph neural network variants have been
proposed, most following a neighborhood aggregation scheme, where the node
features are updated via aggregating features of its neighboring nodes from
layer to layer. Though related research surges, the power of GNNs are still not
on-par-with their counterpart CNNs in computer vision and RNNs in natural
language processing. We rethink this problem from the perspective of
information propagation, and propose to enhance information propagation among
GNN layers by combining heterogeneous aggregations. We argue that as richer
information are propagated from shallow to deep layers, the discriminative
capability of features formulated by GNN can benefit from it. As our first
attempt in this direction, a new generic GNN layer formulation and upon this a
new GNN variant referred as HAG-Net is proposed. We empirically validate the
effectiveness of HAG-Net on a number of graph classification benchmarks, and
elaborate all the design options and criterions along with.
