The　challenges　posed　by　heterogeneous　data　in　practical　applications　have　made　multiview　semi-supervised　classification　a　focus　of　attention　for　researchers.　While　several　graph-based　approaches　have　been　suggested　for　this　task,　they　tend　to　use　homogeneous　feature　propagation,　leading　to　even　diffusion　of　node　information　to　their　neighbors.　However,　this　diffusion　strategy　results　in　nodes　acquiring　information　of　equal　proportion　from　dissimilar　samples.　In　this　article,　we　propose　a　solution　to　address　these　issues　by　introducing　a　graph　diffusion-induced　network　for　multiview　semi-supervised　classification.　By　formulating　a　discretized　partial　differential　equation　on　a　manifold,　we　derive　a　nonlinear　and　inhomogeneous　diffusion　equation　to　govern　information　propagation　on　the　graph.　Then,　we　investigate　the　impact　of　various　nonlinear　activation　functions　on　random　switching　edge　directions　and　their　suppressive　effects　on　information　diffusion　between　different　nodes.　In　addition,　the　cross-view　consistency　under　the　semi-supervised　scenarios　is　defined　and　guaranteed　for　better　information　fusion.　The　comprehensive　experimental　results　demonstrate　the　superiority　of　the　proposed　method　compared　with　state-of-the-art　approaches.　The　effectiveness　of　the　proposed　approach　in　handling　diverse　and　heterogeneous　data　showcases　its　potential　for　advancing　multiview　semi-supervised　classification　techniques.　©　2012　IEEE.