Viral　information　like　rumors　or　fake　news　is　spread　over　a　communication　network　like　a　virus　infection　in　a　unidirectional　manner:　entity　i　conveys　information　to　a　neighbor　j,　resulting　in　two　equally　informed　(infected)　parties.　Existing　graph　diffusion　processes　focus　only　on　bidirectional　diffusion　on　an　undirected　graph.　Instead,　leveraging　recent　research　in　graph　signal　processing　(GSP),　we　propose　a　new　directed　acyclic　graph　(DAG)　diffusion　process　to　estimate　the　probability　x(i)(t)　of　node　i＇s　infection　at　time　t　given　an　initial　infected　source　node　s,　where　x(i)(infinity)　=　1.　Specifically,　given　an　undirected　positive　graph　modeling　node-to-node　communication,　we　first　estimate　its　graph　embedding:　a　latent　coordinate　for　each　graph　node　in　an　assumed　low-dimensional　manifold　space　via　extreme　eigenvectors　computed　using　LOBPCG.　Next,　we　construct　a　DAG　based　on　Euclidean　distances　between　latent　coordinates.　Spectrally,　we　prove　that　the　asymmetric　DAG　Laplacian　matrix　contains　real　non-negative　eigenvalues,　and　that　the　DAG　diffusion　converges　to　the　all-infection　vector　x(infinity)　=　1　as　t　-＞　infinity.　Simulations　show　that　our　DAG　diffusion　process　accurately　estimates　the　probabilities　of　node　infection　over　a　variety　of　graph　structures　at　different　time　instants.