Definitions of dimension

The most obvious way to define the dimension of a space is somehow to ask how many parameters—or coordinates—are needed to specify a point in it. But starting in the 1870s the discovery of constructs like space-filling curves (see page 1127) led to investigation of other definitions. And indeed there is some reason to believe that around 1884 Georg Cantor may have tried developing a definition based on essentially the idea that I use here of looking at growth rates of volumes of spheres (balls). But for standard continuous spaces this definition is hard to make robust—since unlike in discrete networks where one can define volume just by counting nodes, defining volume in a continuous space requires assigning a potentially arbitrary density function. And as a result, in the late 1800s and early 1900s other definitions of dimension were developed. What emerged as most popular is topological dimension, in which one fills space with overlapping balls, and asks what the minimum number that ever have to overlap at any point will be. Also considered was so-called Hausdorff dimension, which became popular in connection with fractals in the 1980s (see page 933), and which can have non-integer values. But for discrete networks the standard definitions for both topological and Hausdorff dimension give the trivial result 0. One can get more meaningful results by thinking about continuum limits, but the definition of dimension that I give in the main text seems much more straightforward. Even here, there are however some subtleties. For example, to find a definite volume growth rate one does still need to take some kind of limit—and one needs to avoid sampling too many or too few nodes in the network. And just as with fractal dimensions discussed on page 933 there are issues about whether a definite power law for the growth rate will emerge, and how one should average over results for different parts of the network. There are some alternative approaches to defining dimension in which some of these issues at least become less explicit. For example, one can imagine not just forming a ball on the network, but instead growing something like a cellular automaton, and seeing how big a pattern it produces after some number of steps. And similarly, one can for example look at the statistics of random walks on the network. A slightly different but still related approach is to study the density of eigenvalues of the Laplace operator—which can also be thought of as measuring the number of solutions to equations giving linear constraints on numbers assigned to connected nodes. More sophisticated versions of this involve looking at invariants studied in topological field theory. And there are potentially also definitions based for example on considering geodesics and seeing how many linearly independent directions can be defined with them. (Note that given explicit coordinates, one can check whether one is in d or more dimensions by asking for all possible points

Det[Table[(x[i] - x[j]) . (x[i] - x[j]), {i, d + 3}, {j, d + 3}]]  0

and this should also work for sufficiently separated points on networks. Still another related approach is to consider coloring the edges of a network: if there are d + 1 possible colors, all of which appear at every node, then it follows that d coordinates can consistently be assigned to each node.)