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In our daily lives, images and texts are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image and text collections. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visualdoi:10.1117/12.2074198 dblp:conf/vda/GuWMNK15 fatcat:tpsmcsbwkrczllhfz23rzsjkdy