Robust subgraph counting with distribution-free random graph analysis

Abstract

Subgraphs such as cliques, loops and stars form crucial connections in the topologies of real-world networks. Random graph models provide estimates for how often certain subgraphs appear, which in turn can be tested against real-world networks. These subgraph counts, however, crucially depend on the assumed degree distribution. Fitting a degree distribution to network data is challenging, in particular for scale-free networks with power-law degrees. In this paper we develop robust subgraph counts that do not depend on the entire degree distribution, but only on the mean and mean absolute deviation (MAD), summary statistics that are easy to obtain for most real-world networks. By solving an optimization problem, we provide tight (the sharpest possible) bounds for the subgraph counts, for all possible subgraphs, and for all networks with degree distributions that share the same mean and MAD. We identify the extremal random graph that attains the tight bounds as the graph with a specific three-point degree distribution. We leverage the bounds to obtain robust scaling laws for how the numbers of subgraphs grow as function of the network size. The scaling laws indicate that sparse power-law networks are not the most extreme networks in terms of subgraph counts, but dense power-law networks are. The robust bounds are also shown to hold for several real-world data sets.

arXiv comment14 pages

References in corpus (24)

• Power-law distributions in empirical data
• The structure of scientific collaboration networks
• The large-scale organization of metabolic networks
• Epidemic processes in complex networks
• Intensity and coherence of motifs in weighted complex networks
• Scale-free topology of e-mail networks
• Scale-free networks are rare
• Generation of uncorrelated random scale-free networks
• Large-scale topological and dynamical properties of Internet
• The statistical mechanics of networks
• The phase transition in inhomogeneous random graphs
• Generating simple random graphs with prescribed degree distribution
• Cut-offs and finite size effects in scale-free networks
• Truncation of power law behavior in "scale-free" network models due to information filtering
• Subgraphs in random networks
• Analytical maximum-likelihood method to detect patterns in real networks
• Scale-free Networks Well Done
• Loops of any size and Hamilton cycles in random scale-free networks
• Number of cliques in random scale-free network ensembles
• Counting cliques and cycles in scale-free inhomogeneous random graphs
• Clustering of random scale-free networks
• Local clustering in scale-free networks with hidden variables
• Variational principle for scale-free network motifs
• Fluctuations analysis in complex networks modeled by hidden variable models. Necessity of a large cut-off in hidden-variable models