Abstract
Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).
Highlights
At the core of any scientific pursuit stands a correspondence between objects of interest and an appropriate representation, most commonly a mathematical one
Networks with ground-truth meso-scale structures were simulated, and different models were compared based on their model fit
In the first set of experiments, we tested the capability of the method in inferring the true number of communities in the network, both for community structure (CS) and the hybrid structure 2 (HS2)
Summary
At the core of any scientific pursuit stands a correspondence between objects of interest and an appropriate representation, most commonly a mathematical one. The way we represent objects in our models determines both the syntactical characteristics and semantic scope of the formalization used in the scientific modeling. Most systems of interest in social, biological, and physical sciences today consist of structurally organized objects and their interactions. Many questions regarding the current and future states of such systems pertain to their architectural properties. This fact naturally explains the increasing popularity of network representations as favored in various domains of science. Networks are used to represent structured systems by referring to each object as a node and an interaction between a pair of objects as an edge between two nodes [1]
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