Three-dimensional (3D) genome organization, which determines how the DNA is packaged inside the nucleus, has emerged as a key regulatory mechanism of cellular processes. High-throughput chromosomal conformation capture (Hi-C) technologies have enabled the study of 3D genome organization by experimentally measuring interactions among genomic regions in 3D space. Analysis of Hi-C data has revealed higher-order organizational units at multiple resolutions: chromosomal territories, compartments, and topologically associating domains (TADs). Changes or disruptions to such structures have been associated with disease, development, and evolution. Therefore, a key problem is to systematically detect higher-order structural changes across Hi-C datasets from multiple conditions. Existing computational methods to detect changes in 3D genome organization either do not model higher-order structural units, specialize only in a specific scale (e.g., TADs), or only compare pairs of Hi-C datasets. We address these limitations with Tree-structured Graph-regularized Integrated Factorization (TGIF), a new multi-task Non-negative Matrix Factorization (NMF) approach. TGIF makes use of complex tree-structured relationships among multiple Hi-C datasets such that closely related tasks, one for each Hi-C matrix, have similar lower-dimensional factors. The factors can be further constrained with task-specific graph regularization and are used to extract clusters of genomic regions with dynamically changing interaction profiles across tasks. We demonstrate our framework effectively recovers ground-truth clusters in simulated data and can detect biologically meaningful structural changes in Hi-C datasets from cancer cell lines and mouse neural development at genome-wide, compartmental, and local TAD scales.