Stability in flux: community structure in dynamic networks

Modelling transitions between egalitarian, dynamic leader and absolutist power structures

Human groups show a variety of leadership dynamics ranging from egalitarian groups with no leader, to groups with changing leaders, to absolutist groups with a single long-term leader. Here, we model transitions between these different phases of leadership dynamics, investigating the role of inequalities in relationships between individuals. Our results demonstrate a novel riches-to-rags class of leadership dynamics where a leader can be replaced by a new individual. We note that the transition between the three different phases of leadership dynamics resembles transitions in leadership dynamics during the Neolithic period of human history. We argue how technological developments, such as food storage and/or weapons which allow one individual to control large quantities of resources, would mean that relationships became more unequal. In general terms, we provide a model of how individual relationships can affect leadership dynamics and structures.

Self-Organization and Artificial Life

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Underlying socio-political processes behind the 2016 US election

Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated ‘bot’ accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes.

A gap junction inhibitor, carbenoxolone, induces spatiotemporal dispersion of renal cortical perfusion and impairs autoregulation

Renal autoregulation dynamics originating from the myogenic response (MR) and tubuloglomerular feedback (TGF) can synchronize over large regions of the kidney surface, likely through gap junction-mediated electrotonic conduction and reflecting distributed operation of autoregulation. We tested the hypotheses that inhibition of gap junctions reduces spatial synchronization of autoregulation dynamics, abrogates spatial and temporal smoothing of renal perfusion, and impairs renal autoregulation. In male Long-Evans rats, we infused the gap junction inhibitor carbenoxolone (CBX) or the related glycyrrhizic acid (GZA) that does not block gap junctions into the renal artery and monitored renal blood flow (RBF) and surface perfusion by laser speckle contrast imaging. Neither CBX nor GZA altered RBF or mean surface perfusion. CBX preferentially increased spatial and temporal variation in the distribution of surface perfusion, increased spatial variation in the operating frequencies of the MR and TGF, and reduced phase coherence of TGF and increased its dispersion. CBX, but not GZA, impaired dynamic and steady-state autoregulation. Separately, infusion of the Rho kinase inhibitor Y-27632 paralyzed smooth muscle, grossly impaired dynamic autoregulation, and monotonically increased spatial variation of surface perfusion. These data suggest CBX inhibited gap junction communication, which in turn reduced the ability of TGF to synchronize among groups of nephrons. The results indicate that impaired autoregulation resulted from degraded synchronization, rather than the reverse. We show that network behavior in the renal vasculature is necessary for effective RBF autoregulation.

Reconstructing propagation networks with temporal similarity

Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

Laser speckle contrast imaging reveals large-scale synchronization of cortical autoregulation dynamics influenced by nitric oxide

Synchronization of tubuloglomerular feedback (TGF) dynamics in nephrons that share a cortical radial artery is well known. It is less clear whether synchronization extends beyond a single cortical radial artery or whether it extends to the myogenic response (MR). We used LSCI to examine cortical perfusion dynamics in isoflurane-anesthetized, male Long-Evans rats. Inhibition of nitric oxide synthases by N(ω)-nitro-l-arginine methyl ester (l-NAME) was used to alter perfusion dynamics. Phase coherence (PC) was determined between all possible pixel pairs in either the MR or TGF band (0.09-0.3 and 0.015-0.06 Hz, respectively). The field of view (≈4 × 5 mm) was segmented into synchronized clusters based on mutual PC. During the control period, the field of view was often contained within one cluster for both MR and TGF. PC was moderate for TGF and modest for MR, although significant in both. In both MR and TGF, PC exhibited little spatial variation. After l-NAME, the number of clusters increased in both MR and TGF. MR clusters became more strongly synchronized while TGF clusters showed small highly coupled, high-PC regions that were coupled with low PC to the remainder of the cluster. Graph theory analysis probed modularity of synchronization. It confirmed weak synchronization of MR during control that probably was not physiologically relevant. It confirmed extensive and long-distance synchronization of TGF during control and showed increased modularity, albeit with larger modules seen in MR than in TGF after l-NAME. The results show widespread synchronization of MR and TGF that is differentially affected by nitric oxide.

Community structure and multi-modal oscillations in complex networks

In many types of network, the relationship between structure and function is of great significance. We are particularly interested in community structures, which arise in a wide variety of domains. We apply a simple oscillator model to networks with community structures and show that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we show that such global oscillations may arise as a direct result of network topology. We also observe that additional modes of oscillation (as detected through frequency analysis) occur in networks with additional levels of topological hierarchy and that such modes may be directly related to network structure. We apply the method in two specific domains (metabolic networks and metropolitan transport) demonstrating the robustness of our results when applied to real world systems. We conclude that (where the distribution of oscillator frequencies and the interactions between them are known to be unimodal) our observations may be applicable to the detection of underlying community structure in networks, shedding further light on the general relationship between structure and function in complex systems.

The interplay between social networks and culture: theoretically and among whales and dolphins

Culture is increasingly being understood as a driver of mammalian phenotypes. Defined as group-specific behaviour transmitted by social learning, culture is shaped by social structure. However, culture can itself affect social structure if individuals preferentially interact with others whose behaviour is similar, or cultural symbols are used to mark groups. Using network formalism, this interplay can be depicted by the coevolution of nodes and edges together with the coevolution of network topology and transmission patterns. We review attempts to model the links between the spread, persistence and diversity of culture and the network topology of non-human societies. We illustrate these processes using cetaceans. The spread of socially learned begging behaviour within a population of bottlenose dolphins followed the topology of the social network, as did the evolution of the song of the humpback whale between breeding areas. In three bottlenose dolphin populations, individuals preferentially associated with animals using the same socially learned foraging behaviour. Homogeneous behaviour within the tight, nearly permanent social structures of the large matrilineal whales seems to result from transmission bias, with cultural symbols marking social structures. We recommend the integration of studies of culture and society in species for which social learning is an important determinant of behaviour.

Using network theory to identify the causes of disease outbreaks of unknown origin

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic.

Animal social networks as substrate for cultural behavioural diversity

We used individual-based stochastic models to examine how social structure influences the diversity of socially learned behaviour within a non-human population. For continuous behavioural variables we modelled three forms of dyadic social learning, averaging the behavioural value of the two individuals, random transfer of information from one individual to the other, and directional transfer from the individual with highest behavioural value to the other. Learning had potential error. We also examined the transfer of categorical behaviour between individuals with random directionality and two forms of error, the adoption of a randomly chosen existing behavioural category or the innovation of a new type of behaviour. In populations without social structuring the diversity of culturally transmitted behaviour increased with learning error and population size. When the populations were structured socially either by making individuals members of permanent social units or by giving them overlapping ranges, behavioural diversity increased with network modularity under all scenarios, although the proportional increase varied considerably between continuous and categorical behaviour, with transmission mechanism, and population size. Although functions of the form e(c)¹(m)⁻(c)² + (c)³(Log(N)) predicted the mean increase in diversity with modularity (m) and population size (N), behavioural diversity could be highly unpredictable both between simulations with the same set of parameters, and within runs. Errors in social learning and social structuring generally promote behavioural diversity. Consequently, social learning may be considered to produce culture in populations whose social structure is sufficiently modular.