Heterogeneous Graph Alignment for Joint Reasoning and Interpretability
Zahra Moslemi, Ziyi Liang, Norbert Fortin, Babak Shahbaba

TL;DR
The paper introduces MGMT, a scalable and interpretable framework for joint reasoning across heterogeneous graphs, effectively aligning and integrating information without shared node identities, demonstrated on synthetic and neuroscience data.
Contribution
It proposes a novel Meta-Graph Transformer that unifies multi-graph learning, enabling cross-graph reasoning and interpretability in heterogeneous graph collections.
Findings
MGMT outperforms state-of-the-art models in graph-level prediction tasks.
Provides interpretable insights through supernodes and superedges.
Effective on both synthetic and real-world neuroscience datasets.
Abstract
Multi-graph learning is crucial for extracting meaningful signals from collections of heterogeneous graphs. However, effectively integrating information across graphs with differing topologies, scales, and semantics, often in the absence of shared node identities, remains a significant challenge. We present the Multi-Graph Meta-Transformer (MGMT), a unified, scalable, and interpretable framework for cross-graph learning. MGMT first applies Graph Transformer encoders to each graph, mapping structure and attributes into a shared latent space. It then selects task-relevant supernodes via attention and builds a meta-graph that connects functionally aligned supernodes across graphs using similarity in the latent space. Additional Graph Transformer layers on this meta-graph enable joint reasoning over intra- and inter-graph structure. The meta-graph provides built-in interpretability:…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Explainable Artificial Intelligence (XAI) · Machine Learning in Healthcare
