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I am working on a problem where I have to model a graph topology, where the nodes are logic/arithmetic operations that can be applied to the input. The network receives a multi-dimensional input, and returns a multi-dimensional processed output with less dimensions. The only things I have to do this are a set of input/output pairs and a set of probable nodes.

The system itself follows a few constraints:

  • The nodes are unique, so there is a limited amount of possible subsets that can compose the graph.
  • The input must enter a single node, and the output must be returned from a single node, even if the path of the information branches within the network. In case multiple topologies are possible, the minimal is the answer.

There are also a few aggravants:

  • Since the nodes are a "probable list", there is no guarantee that the system is possible, but it is important to try to find the closest match possible, as a machine learning system uses the closeness to satisfaction to find new sets of probable nodes.
  • Nodes might receive multiple or just one input, and they might output one or multiple streams of output.

Given this system, I am wondering if anyone here knows of an existing algorithm that solves this problem(or something close that can be adapted).

My question thus is as follows:

Given a set of arithmetic and logical functions and a set of input/output pairs, is there an algorithm to find a graph of functions that satisfies all inputs/output pairs?

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  • $\begingroup$ May be try bayesian networks. ... $\endgroup$ Dec 29 '19 at 15:28
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That problem is commonly called dataflow programming, a directed graph of the data flowing between operations.

There are many options to find valid data flow graphs:

  1. Brute force - Enumerate all possible graphs, then evaluate them.
  2. Beam search - Create likely candidates, then evaluate them to explore the space.
  3. Architecture search - Since TensorFlow is a dataflow programming language, complete Neural Architecture Search (NAS) within its language to find valid graphs.
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