🤖 AI Summary
This work addresses the challenge of joint reasoning across heterogeneous graphs in the absence of shared node identities by proposing the Multi-Graph Meta Transformer (MGMT) framework. MGMT maps individual graphs into a shared latent space using graph transformers and employs an attention mechanism to select task-relevant hypernodes. Cross-graph meta-graphs are then constructed based on latent similarities to enable joint inference. Notably, MGMT introduces an interpretable meta-graph structure that explicitly reveals cross-graph alignment relationships and critical substructures through hypernodes and hyperedges, enhancing scientific interpretability without compromising performance. Experiments demonstrate that the method outperforms state-of-the-art models on both synthetic benchmarks and real-world neuroscience tasks, while providing interpretable representations that support scientific discovery.
📝 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: supernodes and superedges highlight influential substructures and cross-graph alignments. Evaluating MGMT on both synthetic datasets and real-world neuroscience applications, we show that MGMT consistently outperforms existing state-of-the-art models in graph-level prediction tasks while offering interpretable representations that facilitate scientific discoveries. Our work establishes MGMT as a unified framework for structured multi-graph learning, advancing representation techniques in domains where graph-based data plays a central role.