Theory and algorithms for clusters of cycles in graphs for material networks
Perrin E. Ruth, Maria K. Cameron

TL;DR
This paper introduces a novel method for sampling minimal cycle bases in complex material networks, enabling better analysis of cycle clusters and their structures in large graphs.
Contribution
It advances the theory of graph cycles by proposing a uniform sampling method for MCBs and introduces a dual graph framework for analyzing cycle intersections.
Findings
Sampled MCBs are statistically well-defined and proportional to edge count.
Applied method reveals small polyhedral structures related to diamond in hydrocarbon networks.
The number of relevant cycles can grow exponentially with system size.
Abstract
Analysis of complex networks, particularly material networks such as the carbon skeleton of hydrocarbons generated in hydrocarbon pyrolysis in carbon-rich systems, is essential for effectively describing, modeling, and predicting their features. An important and the most challenging part of this analysis is the extraction and effective description of cycles, when many of them coalesce into complex clusters. A deterministic minimum cycle basis (MCB) is generally non-unique and biased to the vertex enumeration. The union of all MCBs, called the set of relevant cycles, is unique, but may grow exponentially with the graph size. To resolve these issues, we propose a method to sample an MCB uniformly at random. The output MCB is statistically well-defined, and its size is proportional to the number of edges. We review and advance the theory of graph cycles from previous works of Vismara,…
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Taxonomy
TopicsComplex Network Analysis Techniques · Graph theory and applications · Topological and Geometric Data Analysis
