MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes
cs.LG
Submitted: 2024-11-29
Updated: 2026-08-31
Comments: Accepted for publication in TMLR, 2026
Code: https://github.com/hcagri/MEGA-GNN
Project page: https://iclr-blogposts.github.io/2023
License: http://creativecommons.org/licenses/by/4.0/
The gist: Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world systems.
Terminology
Abstract
Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world systems. In these graphs, effective learning requires preserving information from repeated interactions while distinguishing contributions from different neighbors. Existing neural network solutions for edge-attributed multigraphs remain limited: some lose information from repeated interactions, while others break permutation equivariance. To address this, we introduce neighbor-aware aggregation, an operator that first combines multi-edge features for each neighbor and then aggregates across neighbors. This operator captures per-neighbor statistics that standard single-stage aggregation cannot represent. Building on this operator, we present MEGA-GNN, a model-agnostic message-passing framework for edge-attributed multigraphs. We show that MEGA-GNN is permutation equivariant and has the same asymptotic complexity as standard GNNs with edge updates. We evaluate our approach on datasets from social networks and financial transaction networks. Neighbor-aware aggregation consistently improves GNN performance and matches or surpasses state-of-the-art methods.
Sources
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
- edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
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