  Online stores often utilize product relationships such as bundles and
substitutes to improve their catalog quality and guide customers through myriad
choices. Entity resolution using pairwise product matching models offers a
means of inferring relationships between products. In mature data repositories,
the relationships may be mostly correct but require incremental improvements
owing to errors in the original data or in the entity resolution system. It is
critical to devise incremental entity resolution (IER) approaches for improving
the health of relationships. However, most existing research on IER focuses on
the addition of new products or information into existing relationships.
Relatively little research has been done for detecting low quality within
current relationships.
  This paper proposes a novel method for identifying inconsistent clusters
(IC), existing groups of related products that do not belong together. We
propose to treat the identification of inconsistent clusters as a supervised
learning task which predicts whether a graph of products with similarities as
weighted edges should be partitioned into multiple clusters. In this case, the
problem becomes a classification task on weighted graphs and represents an
interesting application area for modern tools such as Graph Neural Networks
(GNNs). We demonstrate that existing Message Passing neural networks perform
well at this task, exceeding traditional graph processing techniques. We also
develop a novel message aggregation scheme for Message Passing Neural Networks
that further improves the performance of GNNs on this task. We apply the model
to synthetic datasets, a public benchmark dataset, and an internal application.
Our results demonstrate the value of graph classification in IER and the
ability of graph neural networks to develop useful representations for graph
partitioning.
