  Graph convolutional neural networks (GCNNs) have been attracting increasing
research attention due to its great potential in inference over graph
structures. However, insufficient effort has been devoted to the aggregation
methods between different convolution graph layers. In this paper, we introduce
a graph attribute aggregation network (GAAN) architecture. Different from the
conventional pooling operations, a graph-transformation-based aggregation
strategy, progressive margin folding, PMF, is proposed for integrating graph
features. By distinguishing internal and margin elements, we provide an
approach for implementing the folding iteratively. And a mechanism is also
devised for preserving the local structures during progressively folding. In
addition, a hypergraph-based representation is introduced for transferring the
aggregated information between different layers. Our experiments applied to the
public molecule datasets demonstrate that the proposed GAAN outperforms the
existing GCNN models with significant effectiveness.
