Percolation in networks with local homeostatic plasticity

Percolation is a process that impairs network connectedness by deactivating links or nodes. This process features a phase transition that resembles paradigmatic critical transitions in epidemic spreading, biological networks, traffic and transportation systems. Some biological systems, such as networks of neural cells, actively respond to percolation-like damage, which enables these structures to maintain their function after degradation and aging. Here we study percolation in networks that actively respond to link damage by adopting a mechanism resembling synaptic scaling in neurons. We explain critical transitions in such active networks and show that these structures are more resilient to damage as they are able to maintain a stronger connectedness and ability to spread information. Moreover, we uncover the role of local rescaling strategies in biological networks and indicate a possibility of designing smart infrastructures with improved robustness to perturbations.

Overcoming the liability of distance? An exploratory study of the associations between social networks, sense of community and spatial colocation

Purpose This study aims to examine the associations of social networks with the sense of community (SOC) construct and spatial colocation or having an office. The study site was an institute for health-care policy research formed in 2011 by bringing together scientists from more than 20 different university units. Only 30% of the scientists were had an office or physical presence at the institute. Therefore, the institute was an ideal site to examine whether SOC was correlated with different dimensions of network position – connectedness, reachability and brokerage – even when the authors account for the lack of spatial colocation for the off-site scientists. Design/methodology/approach A two-part (sociometric and workplace) internet survey instrument was administered in 2014 to the institute’s population of 411 individuals. The sociometric data were used to create an undirected interaction network and the following dependent variables (DVs) or network centralities: normalized degree to measure connectedness; average reciprocal distance to capture reachability; and normalized betweenness to proxy brokerage. Separate node-level network regressions were then run with random permutations (N = 10,000) and listwise deletion for each of the DVs with SOC and spatial colocation as the independent variables, and variables that controlled for gender, organizational affiliation and job category. Findings SOC and spatial colocation are both positively and significantly correlated with network connectedness and reachability. The results suggest that both SOC and spatial colocation have a larger impact on reachability than connectedness. However, neither SOC nor spatial colocation are significantly associated with network brokerage. Finally, the findings show that SOC and spatial colocation are more reliable predictors of network connectedness and reachability than are key individual- and unit-level control variables, specifically the individual’s sex, job category and organizational affiliation. The controls were not significantly associated with any of the three network centralities, namely, connectedness, reachability and brokerage. Originality/value This exploratory study used social network analysis and node-level network regressions to examine the associations from SOC and spatial colocation to dimensions of network position. SOC is positively and significantly associated with network connectedness and reachability, suggesting that SOC is an important consideration when individuals are disadvantaged from the absence of spatial colocation. The findings have implications for work in the context of the COVID-19 pandemic as they imply that interventions based on the SOC construct could potentially lessen the negative effects of remote work on workplace social networks due to factors such as the reduction of social contacts.

Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach

This work analyzes the frequency-dependent network structure of Economic Policy Uncertainties (EPU) across G-7 countries between January 1998 and April 2021. We implement an approach that builds dynamic networks relying on a locally stationary Time-Varying Parameter-Vector Autoregressive model using Quasi-Bayesian Local Likelihood methods. We compute short-, medium-, and long-term network connectedness of G-7 EPUs over a period covering several economic/financial turmoils. Furthermore, we structure short-term network topologies for the Global Financial Crisis (GFC) and the COVID-19 pandemic periods. Findings of the study indicate amplified interdependencies between G-7 EPUs around well-known economic/geopolitical incidents, frequency-dependent connectedness networks among them, and stronger interdependencies than the medium-, and long-term linkages. Finally, we find that short-term spillovers are not persistent in the long-term for both turmoil periods.

Resource availability influences global social network properties in Gunnison’s prairie dogs (Cynomys gunnisoni)

Increasingly we are discovering that the interactions between individuals within social groups can be quite complex and flexible. Social network analysis offers a toolkit to describe and quantify social structure, the patterns we observe, and evaluate the social and environmental factors that shape group dynamics. Here, we used 14 Gunnison’s prairie dogs networks to evaluate how resource availability and network size influenced four global properties of the networks (centralization, clustering, average path length, small word index). Our results suggest a positive correlation between overall network cohesion and resource availability, such that networks became less centralized and cliquish as biomass/m2 availability decreased. We also discovered that network size modulates the link between social interactions and resource availability and is consistent with a more ‘decentralized’ group. This study highlights the importance of how individuals modify social cohesions and network connectedness as a way to reduce intragroup competition under different ecological conditions.

Female social structure influences, and is influenced by, male introduction and integration success among captive rhesus macaques (Macaca mulatta)

Animal social structure is influenced by multiple socioecological factors. Of these, the links between changes to group demography through the arrival of new individuals and residents’ social structure remain unclear. Across seven groups of captive rhesus macaques (Macaca mulatta), we examine how male introductions may be influenced by, and in-turn influence, aspects of female social structure. GLMMs revealed that males integrated more successfully into groups in which females showed more ‘despotic’ social structures, i.e., higher aggression rates, steeper dominance hierarchies, and greater rank-skew in allogrooming network connectedness. Yet during periods that followed males’ social integration, females increased their social tolerance (decreased aggression and shallower hierarchies) and group cohesivity (less clustered allogrooming networks), but retained their tendencies to groom dominants. Our findings, independent of group size and matrilineal relatedness, help better understand how dispersal/immigration may influence social structure, and how assessing changes to social structure may inform macaque welfare and management.