Multimodal Large Language Models (MLLMs) contain a substantial amount of factual knowledge, which may become outdated or inaccurate over time. Consequently, various knowledge editing techniques have been proposed to update the knowledge encoded within these models. Previous approaches maintain modality consistency during both the editing and testing phases. However, in practical applications, it is desirable for knowledge to be transferable across different modalities, which can enhance the robustness of knowledge editing and potentially allow for cost-effective editing of multimodal knowledge using textual information. To address this, we introduce the concept of Transitivity of Multimodal Knowledge Editing (TMKE) and design corresponding evaluation criteria. Subsequently, we construct a corresponding TMKE Benchmark through an automated pipeline. We evaluate three MLLMs and five knowledge editing methods, uncovering limitations in the current models and methods concerning transitivity. Additionally, we analyze the intrinsic representations of the model during the editing process based on Knowledge Neurons to interpret the experimental phenomena.