The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.

 

Oldham S, Fulcher B, Parkes L, Arnatkevic̆iūtė A, Suo C, Fornito A (2019) Consistency and differences between centrality measures across distinct classes of networks. PLoS ONE 14(7): e0220061. https://doi.org/10.1371/journal.pone.0220061

Source: journals.plos.org

By exchanging resources, the members of a microbial community can survive in environments where individual species cannot. Despite the abundance of such syntrophy, little is known about its evolutionary origin. The predominant hypothesis is that syntrophy arises when originally independent organisms in the same community become interdependent by accumulating mutations. In this view, syntrophy arises when organisms co-evolve. In sharp contrast we find that different metabolism can interact syntrophically without a shared evolutionary history. We show that syntrophy is an inherent and emergent property of the complex chemical reaction networks that constitute metabolism.

 

Libby E, Hébert-Dufresne L, Hosseini S-R, Wagner A (2019) Syntrophy emerges spontaneously in complex metabolic systems. PLoS Comput Biol 15(7): e1007169.

Source: journals.plos.org

Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler’s social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efficient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

Home-work carpooling for social mixing
Federico Librino, M. Elena Renda, Paolo Santi, Francesca Martelli, Giovanni Resta, Fabio Duarte, Carlo Ratti, Jinhua Zhao

Transportation

Source: link.springer.com

This study explores the way in which traveling paths in ancient cultures are characterized by the relationship between nonlinear shapes and path lengths in terms of distances. In particular, we analyze the case of trade routes that connected Aztec settlements around 1521 CE in central Mexico. Based on the complex systems perspective, we used the least cost path approximation to reconstruct a hypothetical large-scale map of routes reproducing physical connections among ancient places. We compared these connections with different spatial configurations and identified the probability distribution functions of path lengths. We evaluated the nonlinearity using the mean absolute error based on the path fitness of simple linear models. We found asymmetrical distributions and positive relationships between those measures. If a path length increases, so does its nonlinearity. Thus, the simple pattern of traveling in the Aztec region is fairly unlikely to be straight and short. Complex pathways can represent most of the ancient routes in central Mexico.

 

Lugo I, Alatriste-Contreras MG (2019) Nonlinearity and distance of ancient routes in the Aztec Empire. PLoS ONE 14(7): e0218593.

Source: journals.plos.org