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 task-specific 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.