  Though the multiscale graph learning techniques have enabled advanced feature
extraction frameworks, the classic ensemble strategy may show inferior
performance while encountering the high homogeneity of the learnt
representation, which is caused by the nature of existing graph pooling
methods. To cope with this issue, we propose a diversified multiscale graph
learning model equipped with two core ingredients: a graph self-correction
(GSC) mechanism to generate informative embedded graphs, and a diversity
boosting regularizer (DBR) to achieve a comprehensive characterization of the
input graph. The proposed GSC mechanism compensates the pooled graph with the
lost information during the graph pooling process by feeding back the estimated
residual graph, which serves as a plug-in component for popular graph pooling
methods. Meanwhile, pooling methods enhanced with the GSC procedure encourage
the discrepancy of node embeddings, and thus it contributes to the success of
ensemble learning strategy. The proposed DBR instead enhances the ensemble
diversity at the graph-level embeddings by leveraging the interaction among
individual classifiers. Extensive experiments on popular graph classification
benchmarks show that the proposed GSC mechanism leads to significant
improvements over state-of-the-art graph pooling methods. Moreover, the
ensemble multiscale graph learning models achieve superior enhancement by
combining both GSC and DBR.
