  A pooling operation is essential for effective graph-level representation
learning, where the node drop pooling has become one mainstream graph pooling
technology. However, current node drop pooling methods usually keep the top-k
nodes according to their significance scores, which ignore the graph diversity
in terms of the node features and the graph structures, thus resulting in
suboptimal graph-level representations. To address the aforementioned issue, we
propose a novel plug-and-play score scheme and refer to it as MID, which
consists of a \textbf{M}ultidimensional score space with two operations,
\textit{i.e.}, fl\textbf{I}pscore and \textbf{D}ropscore. Specifically, the
multidimensional score space depicts the significance of nodes through multiple
criteria; the flipscore encourages the maintenance of dissimilar node features;
and the dropscore forces the model to notice diverse graph structures instead
of being stuck in significant local structures. To evaluate the effectiveness
of our proposed MID, we perform extensive experiments by applying it to a wide
variety of recent node drop pooling methods, including TopKPool, SAGPool,
GSAPool, and ASAP. Specifically, the proposed MID can efficiently and
consistently achieve about 2.8\% average improvements over the above four
methods on seventeen real-world graph classification datasets, including four
social datasets (IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, and COLLAB), and
thirteen biochemical datasets (D\&D, PROTEINS, NCI1, MUTAG, PTC-MR, NCI109,
ENZYMES, MUTAGENICITY, FRANKENSTEIN, HIV, BBBP, TOXCAST, and TOX21). Code is
available at~\url{https://github.com/whuchuang/mid}.

Machine Learning