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In computer science, a control-flow graph (CFG) is a representation, using graph notation, of all paths that might be traversed through a program during its execution. The control-flow graph was conceived by Frances E. Allen , [ 1 ] who noted that Reese T. Prosser used boolean connectivity matrices for flow analysis before.
In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science , it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks . [ 29 ]
A canonical example of a data-flow analysis is reaching definitions. A simple way to perform data-flow analysis of programs is to set up data-flow equations for each node of the control-flow graph and solve them by repeatedly calculating the output from the input locally at each node until the whole system stabilizes, i.e., it reaches a fixpoint.
In computer science, control-flow analysis (CFA) is a static-code-analysis technique for determining the control flow of a program. The control flow is expressed as a control-flow graph (CFG). For both functional programming languages and object-oriented programming languages , the term CFA, and elaborations such as k -CFA, refer to specific ...
A control-flow diagram (CFD) is a diagram to describe the control flow of a business process, process or review. Control-flow diagrams were developed in the 1950s, and are widely used in multiple engineering disciplines.
Corresponding dominator tree of the control flow graph. In computer science, a node d of a control-flow graph dominates a node n if every path from the entry node to n must go through d. Notationally, this is written as d dom n (or sometimes d ≫ n). By definition, every node dominates itself. There are a number of related concepts:
If a (connected) control-flow graph is considered a one-dimensional CW complex called , the fundamental group of will be (). The value of n + 1 {\displaystyle n+1} is the cyclomatic complexity. The fundamental group counts how many loops there are through the graph up to homotopy, aligning as expected.
Despite the great potential complexity and diversity of biological networks, all first-order network behavior generalizes to one of four possible input-output motifs: hyperbolic or Michaelis–Menten, ultra-sensitive, bistable, and bistable irreversible (a bistability where negative and therefore biologically impossible input is needed to return from a state of high output).