Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper, a novel graph-regularized (G) MCCA is developed to account for such geometry-bearing in- formation via graph regularization in the classical maximum- variance MCCA model. GMCCA minimizes the distance between the sought canonical variables and the common sources, while incorporating the graph-induced prior of these sources. To capture nonlinear dependencies, GMCCA is fur- ther broadened to the graph-regularized kernel (GK) MCCA. Numerical tests using real datasets document the merits of G(K)MCCA in comparison with competing alternatives.