The core operation of current Graph Neural Networks (GNNs) is the aggregation
enabled by the graph Laplacian or message passing, which filters the
neighborhood node information. Though effective for various tasks, in this
paper, we show that they are potentially a problematic factor underlying all
GNN methods for learning on certain datasets, as they force the node
representations similar, making the nodes gradually lose their identity and
become indistinguishable. Hence, we augment the aggregation operations with
their dual, i.e. diversification operators that make the node more distinct and
preserve the identity. Such augmentation replaces the aggregation with a
two-channel filtering process that, in theory, is beneficial for enriching the
node representations. In practice, the proposed two-channel filters can be
easily patched on existing GNN methods with diverse training strategies,
including spectral and spatial (message passing) methods. In the experiments,
we observe desired characteristics of the models and significant performance
boost upon the baselines on 9 node classification tasks