A Tight Expressivity Hierarchy for GNN-Based Entity Resolution in Master Data Management

arXiv:2603.27154v1 Announce Type: cross Abstract: Entity resolution -- identifying database records that refer to the same real-world entity -- is naturally modelled on bipartite graphs connecting entity nodes to their attribute values. Applying a message-passing neural network (MPNN) with all available extensions (reverse message passing, port numbering, ego IDs) incurs unnecessary overhead, since different entity resolution tasks have fundamentally different complexity. For a given matching cr