Adaptive Multi-grained Graph Neural Networks
  Graph Neural Networks (GNNs) have been increasingly deployed in a multitude
of different applications that involve node-wise and graph-level tasks. The
existing literature usually studies these questions independently while they
are inherently correlated. We propose in this work a unified model, Adaptive
Multi-grained GNN (AdamGNN), to learn node and graph level representation
interactively. Compared with the existing GNN models and pooling methods,
AdamGNN enhances node representation with multi-grained semantics and avoids
node feature and graph structure information loss during pooling. More
specifically, a differentiable pooling operator in AdamGNN is used to obtain a
multi-grained structure that involves node-wise and meso/macro level semantic
information. The unpooling and flyback aggregators in AdamGNN is to leverage
the multi-grained semantics to enhance node representation. The updated node
representation can further enrich the generated graph representation in the
next iteration. Experimental results on twelve real-world graphs demonstrate
the effectiveness of AdamGNN on multiple tasks, compared with several competing
methods. In addition, the ablation and empirical studies confirm the
effectiveness of different components in AdamGNN.
