for queries (also modeled as fusion vectors). This component is detailed in Section \[sec:fusionVectorSearch\].

Rank-Fusion Graphs {#sec:fusionGraphExtraction}
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This component produces a fusion graph ${\mathcal{G}}$ for a given query sample $q$, also in terms of $m$ rankers and $n$ response items. A fusion graph is a graph-based encoding of multiple ranks for $q$, that encapsulates and correlates ranks.

We follow the fusion graph formulation from [@Dourado:2019:FG], referred to as *FG*, that defines a mapping function $q \mapsto {\mathcal{G}}$, based on its ranks ${\tau}\in {\mathcal{T}_{q}}$ and ranks’ inter-relationships. The proposed formulation also includes a dissimilarity function for *FG*, and a retrieval model based on fusion graphs. Here, however, we focus on the definition of *FG*, extending its use as part of a rank-based late-fusion approach for representation model in prediction tasks, without these components.

The process is illustrated in Figure \[fig:graphFusionFromSample\]. Given a query $q$, $m$ rankers, and a training set of size $n$, $m$ ranks are generated. These ranks are then normalized to allow for producing the fusion graph ${\mathcal{G}}$ for $q$. In rank normalization, the scores in the ranks generated by dissimilarity-based comparators are converted to similarity-based scores. Besides, all ranks have their scores rescaled to the same interval. 