any tasks in the absence of labeled data.

Two phases are defined. The [*training*]{} phase comprehends the modeling of the train set in terms of multiple rankers, as fusion graphs and then as fusion vectors. This step performs a graph embedding of fusion graphs and the training of a multimodal prediction model. The [*inference*]{} phase refers to the multimodal prediction preceded by a rank-based fusion approach for multimodal representation. The training phase is performed only once, while the inference phase is performed per prediction. The first two components – [*fusion graph extraction*]{} and [*graph embedding*]{} – are used in both phases.

![Proposed graph-based rank fusion for multimodal prediction.[]{data-label="fig:fusionVectorEstimation"}](fusionVectorEstimation){width="0.95\linewidth"}

The [*fusion graph extraction*]{} (component 1 in the figure) generates a fusion graph ${\mathcal{G}}$ for a given query sample $q$. ${\mathcal{G}}$ consists of an aggregated representation of multiple ranks for $q$, thus capturing and correlating information of multiple ranks. This formulation is presented in Section \[sec:fusionGraphExtraction\]. [*Graph Embedding*]{} (2) projects fusion graphs into a vector space model, producing a corresponding fusion vector ${\mathcal{V}}$ for ${\mathcal{G}}$. We propose an embedding formulation in Section \[sec:fusionGraphEmbedding\]. At the end, an [*event predictor*]{} (3) is built based on the response fusion vectors, in order to predict 