  Graph Neural networks (GNNs) have recently become a powerful technique for
many graph-related tasks including graph classification. Current GNN models
apply different graph pooling methods that reduce the number of nodes and edges
to learn the higher-order structure of the graph in a hierarchical way. All
these methods primarily rely on the one-hop neighborhood. However, they do not
consider the higher- order structure of the graph. In this work, we propose a
multi-channel Motif-based Graph Pooling method named (MPool) captures the
higher-order graph structure with motif and local and global graph structure
with a combination of selection and clustering-based pooling operations. As the
first channel, we develop node selection-based graph pooling by designing a
node ranking model considering the motif adjacency of nodes. As the second
channel, we develop cluster-based graph pooling by designing a spectral
clustering model using motif adjacency. As the final layer, the result of each
channel is aggregated into the final graph representation. We perform extensive
experiments on eight benchmark datasets and show that our proposed method shows
better accuracy than the baseline methods for graph classification tasks.
