This chapter is concerned with the notion of entropy as applied to graphs for the purpose of measuring complexity.

Most studies of complexity focus on the execution time or space utilization of algorithms. The execution time of an algorithm is proportional to the number of operations required to produce the output as a function of the input size. Space utilization measures the amount of storage required for computation. Both time and space complexity measure the resources required to perform a computation for a specified input. Measuring the complexity of a mathematical object such as a graph is an exercise in structural complexity. This type of complexity does not deal directly with the costs of computation; rather, it offers insight into the internal organization of an object. The structural complexity of a computer program, for example, may indicate the difficulty of modifying or maintaining the program.

One approach to structural complexity involves the length of a code needed to specify an object uniquely (Kolmogorov complexity). The complexity of a string, for example, is the length of the string's shortest description in a given description language [27]. The approach taken in this chapter centers on finding indices of structure, based on Shannon's entropy measure. Unlike Kolmogorov complexity, such an index captures a particular feature of the structure of an object. The symmetry structure of a graph provides the basis for the index explored here.

The choice ...

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