No-Free-Graph: Learning When Multimodal Data Should Be Graphified

2026-10-01

AI Summary

This study addresses the computational redundancy and uncertain benefits arising from indiscriminate relational structure construction in multimodal graph learning. To this end, we propose MAG-SCOUT, a framework that reframes graph construction from a default preprocessing step into a utility-driven selective decision mechanism. By integrating pre-construction graph evaluation, limited relational evidence collection, and task-specific contribution analysis, MAG-SCOUT quantifies the expected utility and cost of incorporating graph structures prior to their construction. Experimental results demonstrate that the proposed approach reduces graph construction overhead by 33.1% while retaining 96.7% of the positive performance gains. Consequently, this work achieves a Pareto-optimal balance between computational efficiency and predictive performance in multimodal graph learning.

Abstract

Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. Existing studies have made substantial progress on how to construct and optimize graphs, but rarely consider a more fundamental question: whether additional relational structures should be introduced for a given dataset and task. Through empirical studies across diverse datasets, tasks, and graph constructors, we reveal that graphification is not consistently beneficial: introducing relational structures can provide substantial improvements in some cases, while offering limited or even negative gains. This observation motivates a new perspective that graph construction should be treated as a selective decision based on its expected utility rather than a default preprocessing step. To address this issue, we propose MAG-SCOUT, a pre-construction graph assessment framework that estimates whether introducing graph structures is beneficial before generating the complete topology. MAG-SCOUT collects limited relational evidence, analyzes its potential taskspecific contribution, and estimates the expected utility of graphification together with construction cost to make a build-or-skip decision. Extensive experiments across six multimodal datasets, three downstream tasks, and diverse graph constructors demonstrate that MAG-SCOUT effectively identifies when graph structures should be introduced, saving 33.1% of task-macro graph work while retaining 96.7% of held-out positive-gain mass under the pre-registered floor.

Problem

• Multimodal Graph Learning
• Graphification
• No-Free-Graph
• Graph Construction
• Utility Estimation

Innovation

• Multimodal Graph Learning
• Graph Construction Assessment
• Expected Utility Estimation
• Build-or-Skip Decision
• MAG-SCOUT