Abstract: Media analytics refers to interactive exploration of multimedia collections to search for information and gain insight on a topic of interest. Exploring large-scale collections requires an organization of the collection in addition to the description of each item, the latter being mostly relevant for search but not for exploration. We investigate graph-based models of collection, focusing on the hyperlinking step in videos and in news collections. In this talk, I will review recent research activities on the topic in the Linkmedia team at Irisa, Rennes, France. We will discuss several techniques for multimedia hyperlinking, e.g., hierarchical topic models, multimodal topic models, multimodal embeding with symmetrical neural networks. We will also discuss user acceptability of hyperlink navigation, in particular describing a novel nearest neighbor graph construction algorithm with good navigability properties. Recent user tests demonstrated the interest of this type of graphs for news analytics. We will conclude with prespective on a use-case in data journalism that require hyperlinking heterogeneous information sources beyond multimedia content.