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The explosive growth of data has led to a profound revolution in data science, particularly in the field of image processing. Graph techniques provide flexibility and efficiency in capturing geometric structures of the imaging data. Major challenges in graph-related problems include graph representation of high-dimensional data, regularization on graphs, and fast algorithms. This mini-symposium aims to showcase a broad spectrum of topics in graph techniques for image processing. The presentations will focus on theoretical aspects of graph representation, computational advances, as well as applications in imaging sciences.