CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation

Authors: Daohan Su, Hao Liu, Xunkai Li et al.

arXiv · PDF

Summary

The authors study multimodal graph neural networks and find that decoupled architectures (which separate feature propagation from model training) are much more efficient than tightly coupled ones, but suffer from "modal conflict"—cross-modal semantic divergence during propagation and misalignment during aggregation. They propose CAMPA, which injects cross-modal similarity into message passing and uses trajectory-level attention to align features across modalities and propagation hops. Experiments show CAMPA outperforms both coupled and decoupled baselines while staying efficient.

Main takeaways:

• Decoupled graph neural networks (which pre-propagate features separately from training) are faster and more scalable than coupled architectures.
• The bottleneck is modal conflict: independent diffusion causes semantic drift across modalities, and naive fusion fails to align multi-hop feature trajectories.
• CAMPA fixes this with two-stage alignment: cross-modal similarity priors during propagation and trajectory-level self/cross-attention during aggregation.
• The method preserves the efficiency of decoupled architectures while consistently improving performance on diverse benchmarks.
• The approach handles long-range dependencies across both modalities and propagation hops.

Relevance

No obvious connection to my work on LLM personas, fine-tuning, or behavioral installation. This is about graph neural networks on multimodal attributed graphs—a completely different domain and architecture from transformer language models.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses benchmark.

Abstract

arXiv:2605.11468v1 Announce Type: new Abstract: Multimodal Graph Neural Networks (MGNNs) have shown strong potential for learning from multimodal attributed graphs, yet most existing approaches rely on tightly coupled architectures that suffer from prohibitive computational overhead. In this paper, we present a systematic empirical analysis showing that decoupled MGNNs are substantially more efficient and scalable for large-scale graph learning. However, we identify a critical bottleneck in existing decoupled pipelines, namely modal conflict, which arises in both the propagation and aggregation stages. Specifically, independent multi-hop diffusion causes cross-modal semantic divergence during propagation, while naive fusion fails to align multi-hop feature trajectories during aggregation, jointly limiting effective representation learning. To address this challenge, we propose CAMPA, a Cross-modal Aligned Multimodal Propagation & Aggregation framework for decoupled multimodal graph learning. Concretely, CAMPA introduces a two-stage alignment mechanism: (1) cross-modal aligned propagation, which injects cross-modal similarity priors into message passing to preserve semantic consistency without additional parameter overhead; (2) trajectory aligned aggregation, which leverages trajectory-level self-attention and cross-attention to capture and align long-range dependencies across modalities and hops. Extensive experiments on diverse benchmark datasets and tasks demonstrate that CAMPA consistently outperforms strong coupled and decoupled baselines while preserving the efficiency advantages of the decoupled paradigm.