  Representing data by means of graph structures identifies one of the most
valid approach to extract information in several data analysis applications.
This is especially true when multimodal datasets are investigated, as records
collected by means of diverse sensing strategies are taken into account and
explored. Nevertheless, classic graph signal processing is based on a model for
information propagation that is configured according to heat diffusion
mechanism. This system provides several constraints and assumptions on the data
properties that might be not valid for multimodal data analysis, especially
when large scale datasets collected from heterogeneous sources are considered,
so that the accuracy and robustness of the outcomes might be severely
jeopardized. In this paper, we introduce a novel model for graph definition
based on fluid diffusion. The proposed approach improves the ability of
graph-based data analysis to take into account several issues of modern data
analysis in operational scenarios, so to provide a platform for precise,
versatile, and efficient understanding of the phenomena underlying the records
under exam, and to fully exploit the potential provided by the diversity of the
records in obtaining a thorough characterization of the data and their
significance. In this work, we focus our attention to using this fluid
diffusion model to drive a community detection scheme, i.e., to divide
multimodal datasets into many groups according to similarity among nodes in an
unsupervised fashion. Experimental results achieved by testing real multimodal
datasets in diverse application scenarios show that our method is able to
strongly outperform state-of-the-art schemes for community detection in
multimodal data analysis.
