  We consider dataflow architecture for two classes of computations which admit
taking linear combinations of execution runs: probabilistic sampling and
generalized animation. We improve the earlier technique of almost continuous
program transformations by adopting a discipline of bipartite graphs linking
nodes obtained via general transformations and nodes obtained via linear
transformations which makes it possible to develop and evolve dataflow programs
over these classes of computations by continuous program transformations. The
use of bipartite graphs allows us to represent the dataflow programs from this
class as matrices of real numbers and evolve and modify programs by continuous
change of these numbers.
  We develop a formalism for higher-order dataflow programming for this class
of dataflow graphs based on the higher-order matrix elements. Some of our
software experiments are briefly discussed.
