  Graph Pooling technology plays an important role in graph node classification
tasks. Sorting pooling technologies maintain large-value units for pooling
graphs of varying sizes. However, by analyzing the statistical characteristic
of activated units after pooling, we found that a large number of units dropped
by sorting pooling are negative-value units that contain useful information and
can contribute considerably to the final decision. To maintain more useful
information, a novel pooling technology, called Geometric Pooling (GP), was
proposed to contain the unique node features with negative values by measuring
the similarity of all node features. We reveal the effectiveness of GP from the
entropy reduction view. The experiments were conducted on TUdatasets to show
the effectiveness of GP. The results showed that the proposed GP outperforms
the SOTA graph pooling technologies by 1%\sim5% with fewer parameters.
