Multimodal topic modeling aims to learn interpretable topics from collections that combine text with other modalities. A persistent difficulty is lexical incompleteness: the text often records only part of the evidence needed to describe a sample, and concepts that are visible in images may have no direct path into the learned word distributions. In many multimodal topic models, visual features mainly help infer document-level topic mixtures, whereas the decoder is still trained on the incomplete observed text. We introduce TIGNTM, a graph completion framework that turns visual evidence into lexical supervision. TIGNTM builds a corpus graph over textual and visual representations, treats related samples as weak lexical witnesses, and produces sparse completion targets for words missing from the observed text. These targets supervise the same decoder that generates topic words, so visual cues can shape the word distributions that define each topic. To match the geometry of normalized multimodal embeddings, TIGNTM adopts hyperspherical neural topic learning. Experiments on multiple multimodal datasets show consistent improvements in coherence, diversity, and overall topic quality compared with strong neural and multimodal baselines.