Title: MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes
Primary area: learning on graphs and other geometries & topologies
Keywords: graph neural network, multigraph, edge attributes, financial crime, money laundering

Abstract: Many real-world graphs, such as financial transaction networks, are edge-attributed multigraphs that feature multiple edges between the same pair of nodes, each with distinct edge attributes. State-of-the-art neural network solutions operating on such edge-attributed multigraphs either preprocess the multigraph by collapsing its multi-edges into a single edge or introduce auxiliary edge features that compromise permutation equivariance. We introduce MEGA-GNN, a graph neural network (GNN) for edge-attributed multigraphs, which overcomes these limitations by employing a two-stage aggregation process in its message passing layers: first, features of the multi-edges between the same two nodes are aggregated, and then messages from distinct neighbors are combined. We show that MEGA-GNN computes a richer set of statistical features than the GNNs that implement only single-stage aggregation in their message passing layers. We evaluate MEGA-GNN on seven financial transaction network datasets and three temporal user-item interaction datasets, demonstrating significant improvements in minority-class F1 scores for illicit transaction detection and ROC-AUC scores for user state-change prediction, respectively, compared to state-of-the-art methods.

TL;DR: A GNN architecture for edge-attributed multigraphs that integrates two-stage aggregation into its message passing layers.