Graph fractal dimension and structure of fractal networks: a combinatorial perspective Pavel Skums1,3 and Leonid Bunimovich2 1Department of Computer Science, Georgia State University, 1 Park Pl NE, Atlanta, GA, USA, 30303 2School of Mathematics, Georgia Institute of Technology, 686 Cherry St NW, Atlanta, GA, USA, 30313 3Corresponding author. Email:
[email protected] December 25, 2019 Abstract In this paper we study self-similar and fractal networks from the combinatorial perspective. We estab- lish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product (or Prague or Neˇsetˇril-R¨odl)dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colorings and graph Kolmogorov complexity, and analyze the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework to evolu- tionary studies by revealing the growth of self-organization of heterogeneous viral populations over the course of their intra-host evolution, thus suggesting mechanisms of their gradual adaptation to the host's environment. As far as the authors know, the proposed approach is the first theoretical framework for study of network fractality within the combinatorial paradigm. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms. Keywords: Fractal network, Self-similarity, Lebesgue dimension, Hausdorff dimension, Kolmogorov complexity, Graph coloring, Clique, Hypergraph.