Public and philanthropic research funding, publications, and research networks for cancer in the Commonwealth and globally between 2016 and 2023: a comparative analysis.

This Review presents a comprehensive analysis of the amounts and distribution of public and philanthropic global cancer research funding between 2016 and 2023, including patterns of international collaboration and downstream research output, with an emphasis on the Commonwealth. We show that annual investment decreased globally each year, apart from a rise in 2021. Network analysis revealed that grant and publication collaborations between the Commonwealth, the USA, and the EU are facilitated by linkages through a core group of Commonwealth countries, including the UK, Australia, and Canada.

Modelling the mitigation of anti-vaccine opinion propagation to suppress epidemic spread: A computational approach.

Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics.

Opinion Dynamics Explain Price Formation in Prediction Markets.

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics.

On the evolutionary language game in structured and adaptive populations.

We propose an evolutionary model for the emergence of shared linguistic convention in a population of agents whose social structure is modelled by complex networks. Through agent-based simulations, we show a process of convergence towards a common language, and explore how the topology of the underlying networks affects its dynamics. We find that small-world effects act to speed up convergence, but observe no effect of topology on the communicative efficiency of common languages.

Sensing Enhancement on Social Networks: The Role of Network Topology.

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement.

Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents.

Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations with the aim of making hidden parameters more easily inferable. More specifically, we consider a model of networked agents who exchange opinions subject to voting dynamics.

Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics.

Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e.

Estimating Determinants of Attrition in Eating Disorder Communities on Twitter: An Instrumental Variables Approach.

Background: The use of social media as a key health information source has increased steadily among people affected by eating disorders (EDs). Research has examined characteristics of individuals engaging in online communities, whereas little is known about discontinuation of engagement and the phenomenon of participants dropping out of these communities.

Objective: This study aimed to investigate the characteristics of dropout behaviors among eating disordered individuals on Twitter and to estimate the causal effects of personal emotions and social networks on dropout behaviors.

Controlling and enhancing synchronization through adaptive phase lags.

We compare two methods for controlling synchronization in the Kuramoto model on an undirected network. The first is by driving selected oscillators at a desired frequency by coupling to an external driver, and the second is by including adaptive lags-or dynamical frustrations-within the Kuramoto interactions, with the lags evolving according to a dynamics as a function of the reference frequency with an associated time constant. Performing numerical simulations with random regular graphs, we find that above a certain connectivity driving via adaptive lags allows for stronger alignment to the external frequency at a lower value of the time constant compared to the corresponding coupling strength for the externally driven model.

Taylor's power law captures the effects of environmental variability on community structure: An example from fishes in the North Sea.

Taylor's power law (TPL) describes the relationship between the mean and variance in abundance of populations, with the power law exponent considered a measure of aggregation. However, the usefulness of TPL exponents as an ecological metric has been questioned, largely due to its apparent ubiquity in various complex systems. The aim of this study was to test whether TPL exponents vary systematically with potential drivers of animal aggregation in time and space and therefore capture useful ecological information of the system of interest.

Social interactions in online eating disorder communities: A network perspective.

Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online.

Effect of Babassu Natural Filler on PBAT/PHB Biodegradable Blends: An Investigation of Thermal, Mechanical, and Morphological Behavior.

Blending of biodegradable polymers in combination with low-price organic fillers has proven to be a suitable approach to produce cost-effective composites in order to address pollution issues and develop products with superior mechanical properties. In the present research work PBAT/PHB/Babassu composites with 25, 50, and 75% of each polymer and 20% of Babassu were produced by melting extrusion. Their thermal, mechanical, and morphological behavior was investigated by differential scanning calorimetry (DSC), tensile testing, and scanning electron microscopy (SEM).

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model.

In this paper, we investigate influence maximization, or optimal opinion control, in a modified version of the two-state voter dynamics in which a native state and a controlled or influenced state are accounted for. We include agent predispositions to resist influence in the form of a probability with which agents spontaneously switch back to the native state when in the controlled state. We argue that in contrast to the original voter model, optimal control in this setting depends on : For low strength of predispositions , optimal control should focus on hub nodes, but for large , optimal control can be achieved by focusing on the lowest degree nodes.

Multiplex model of mental lexicon reveals explosive learning in humans.

Word similarities affect language acquisition and use in a multi-relational way barely accounted for in the literature. We propose a multiplex network representation of this mental lexicon of word similarities as a natural framework for investigating large-scale cognitive patterns. Our representation accounts for semantic, taxonomic, and phonological interactions and it identifies a cluster of words which are used with greater frequency, are identified, memorised, and learned more easily, and have more meanings than expected at random.

Competitive influence maximization and enhancement of synchronization in populations of non-identical Kuramoto oscillators.

Many networked systems have evolved to optimize performance of function. Much literature has considered optimization of networks by central planning, but investigations of network formation amongst agents connecting to achieve non-aligned goals are comparatively rare. Here we consider the dynamics of synchronization in populations of coupled non-identical oscillators and analyze adaptations in which individual nodes attempt to rewire network topology to optimize node-specific aims.

Topology-dependent rationality and quantal response equilibria in structured populations.

Given that the assumption of perfect rationality is rarely met in the real world, we explore a graded notion of rationality in socioecological systems of networked actors. We parametrize an actors' rationality via their place in a social network and quantify system rationality via the average Jensen-Shannon divergence between the games Nash and logit quantal response equilibria. Previous work has argued that scale-free topologies maximize a system's overall rationality in this setup.

Multiplex lexical networks reveal patterns in early word acquisition in children.

Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.

How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection.

Frustration tuning and perfect phase synchronization in the Kuramoto-Sakaguchi model.

We present an analysis of conditions under which the dynamics of a frustrated Kuramoto-or Kuramoto-Sakaguchi-model on sparse networks can be tuned to enhance synchronization. Using numerical optimization techniques, linear stability, and dimensional reduction analysis, a simple tuning scheme for setting node-specific frustration parameters as functions of native frequencies and degrees is developed. Finite-size scaling analysis reveals that even partial application of the tuning rule can significantly reduce the critical coupling for the onset of synchronization.

Costly advertising and the evolution of cooperation.

In this paper, I investigate the co-evolution of fast and slow strategy spread and game strategies in populations of spatially distributed agents engaged in a one off evolutionary dilemma game. Agents are characterized by a pair of traits, a game strategy (cooperate or defect) and a binary 'advertising' strategy (advertise or don't advertise). Advertising, which comes at a cost [Formula: see text], allows investment into faster propagation of the agents' traits to adjacent individuals.

Short versus long term benefits and the evolution of cooperation in the prisoner's dilemma game.

In this paper I investigate the evolution of cooperation in the prisoner's dilemma when individuals change their strategies subject to performance evaluation of their neighbours over variable time horizons. In the monochrome setting, in which all agents per default share the same performance evaluation rule, weighing past events strongly dramatically enhances the prevalence of cooperators. For co-evolutionary models, in which evaluation time horizons and strategies can co-evolve, I demonstrate that cooperation naturally associates with long-term evaluation of others while defection is typically paired with very short time horizons.

Network reciprocity by coexisting learning and teaching strategies.

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor.

Growth and optimality in network evolution.

We investigate networks whose evolution is governed by the interaction of a random assembly process and an optimization process. In the first process, new nodes are added one at a time and form connections to randomly selected old nodes. In between node additions, the network is rewired to minimize its path length.

The evolution of cooperation on correlated payoff landscapes.

We study the evolution of cooperation in the prisoner's dilemma game on time-invariant heterogeneous payoff landscapes on regular and heterogeneous networks. Correlations in the landscape structure and their implications for the evolution of cooperation are investigated. On regular networks we find that negatively and neutrally correlated payoff landscapes strongly enhance cooperation, while positively correlated landscapes may suppress the evolution of cooperation.