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We consider dataflow architecture for two classes of computations which admittaking linear combinations of execution runs: probabilistic sampling andgeneralized animation. We improve the earlier technique of almost continuousprogram transformations by adopting a discipline of bipartite graphs linkingnodes obtained via general transformations and nodes obtained via lineartransformations which makes it possible to develop and evolve dataflow programsover these classes of computations by continuous program transformations. Theuse of bipartite graphs allows us to represent the dataflow programs from thisclass as matrices of real numbers and evolve and modify programs by continuouschange of these numbers.  We develop a formalism for higher-order dataflow programming for this classof dataflow graphs based on the higher-order matrix elements. Some of oursoftware experiments are briefly discussed.

Dataflow Graphs as Matrices and Programming with Higher-order Matrix Elements