Smoking is a major public health and economic burden, and youth smoking is a critical challenge due to its long-term consequences. While school-based smoking cessation programs are promising, they often fail to optimize resource allocation, limiting their effectiveness. This work addresses this limitation by integrating influence maximization techniques into a smoking-specific epidemiological model on networks. Our approach models the intervention as a zealot node representing a professional counsellor exerting influence on selected individuals to induce smoking cessation. Using smoking prevalence data from an experimental school-based cessation program, we calibrated a budget parameter to ensure practical relevance. We then systematically evaluated different resource allocation strategies, varying the timing of interventions, the fraction of smokers targeted, and the criteria used to select individuals. We find that targeting only a limited fraction of smokers is sufficient to achieve substantial reductions in smoking prevalence. Importantly, strategies prioritizing individuals with lower network degree consistently outperform those focusing on highly connected nodes. By preferentially reaching more peripheral and typically disadvantaged individuals, this approach not only improves cost-effectiveness but also contributes to reducing inequalities in both access to and impact of cessation interventions. By bridging the gap between influence maximization theory and real-world applications, this study contributes a novel methodology to enhance efficiency and equity of public health interventions.

We explore the influence maximisation problem in networks with negative ties. Where prior work has focused on unsigned networks, we investigate the need to consider negative ties in networks while trying to maximise spread in a population - particularly under competitive conditions. Given a signed network we optimise the strategies of a focal controller, against competing influence in the network, using two approaches - either the focal controller uses a sign-agnostic approach or they factor in the sign of the edges while optimising their strategy. We compare the difference in vote-shares (or the share of population) obtained by both these methods to determine the need to navigate negative ties in these settings. More specifically, we study the impact of: (a) network topology, (b) resource conditions and (c) competitor strategies on the difference in vote shares obtained across both methodologies. We observe that gains are maximum when resources available to the focal controller are low and the competitor avoids negative edges in their strategy. Conversely, gains are insignificant irrespective of resource conditions when the competitor targets the network indiscriminately. Finally, we study the problem in a game-theoretic setting, where we simultaneously optimise the strategies of both competitors. Interestingly we observe that, strategising with the knowledge of negative ties can occasionally also lead to loss in vote-shares.

Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.

In the study of influence maximization, most existing research often assumes a one-off resource allocation at the start of a competition. As a result, they overlook the benefits of dynamic, time-sensitive strategies. To overcome this limitation, we propose a novel approach using inter-temporal allocations within a non-progressive voter model to optimize the timing and distribution of limited resources for maximizing opinion spread. Our objective is twofold: (i) to provide understanding of patterns of opinion spread in complex networks subject to inter-temporal control, and (ii) to use insights from (i) to develop optimization strategies that balance resource constraints and temporal dynamics. Specifically, our study examines two scenarios: a constant-opponent setting and a game-theoretical framework. In the constant- opponent setting, we find that network heterogeneity significantly influences optimal campaign timing, with late initiation benefiting short time horizons in heterogeneous networks and early starts favoring longer horizons. To further enhance this strategy, we introduce a node- specific optimization strategy that outperforms uniform approaches, especially under resource constraints. In the game-theoretical framework, our results reveal that resource-rich controllers tend to start campaigns early, while resource-limited controllers strategically delay to counter their opponent's advantage. Through analytical approximations and simulations, we provide insights into the temporal dynamics of influence spread. These findings offer practical guidelines for designing effective influence campaigns in competitive and time-sensitive contexts, with applications in marketing, politics, and public health.

Interdisciplinary research is critical for innovation and addressing complex societal issues. We characterise the interdisciplinary knowledge structure of PubMed research articles in medicine as correlation networks of medical concepts and compare the interdisciplinarity of articles between high-ranking (impactful) and less high-ranking (less impactful) medical journals. We found that impactful medical journals tend to publish research that are less interdisciplinary than less impactful journals. Observing that they bridge distant knowledge clusters in the networks, we find that cancer-related research can be seen as one of the main drivers of interdisciplinarity in medical science. Using signed difference networks, we also investigate the clustering of deviations between high and low impact journal correlation networks. We generally find a mild tendency for strong link differences to be adjacent. Furthermore, we find topic clusters of deviations that shift over time. In contrast, topic clusters in the original networks are static over time and can be seen as the core knowledge structure in medicine. Overall, journals and policymakers should encourage initiatives to accommodate interdisciplinarity within the existing infrastructures to maximise the potential patient benefits from IDR.

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. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions.

Vaccine misinformation is a significant driver of vaccine hesitancy, spreading within social networks as anti-vaccine social contagion. This potentially causes larger disease outbreaks due to the clustering of unprotected individuals influenced by this contagion, who may then choose to remain unvaccinated. In this study, through a coupled agent-based model that integrates vaccine opinions and disease diffusion processes, we design an optimal counter-campaign that spreads positive vaccine information to counteract vaccine misinformation and ultimately suppress the spread of an epidemic. We demonstrate that targeting individuals with anti-vaccine neighbors in their social networks can effectively contain the spread of negative influence when interventions are implemented at early stages. Conversely, once anti-vaccine opinion adopters begin forming larger clusters within the network, shielding bridging regions between clusters becomes crucial in restricting the growth of anti-vaccine communities and thereby controlling the spread of epidemics.

Vaccine misinformation fuels vaccine hesitancy, spreading through social networks and can thus lead to the formation of unprotected communities, increasing the risk of larger-scale disease outbreaks. In this study, through an agent-based model that integrates coupled diffusion processes–vaccine opinions and disease diffusion–we design counter-campaigns that counteract vaccine misinformation to promote vaccine uptake aiming to curb the spread of an epidemic. We frame this as an optimization problem, developing adaptive targeting strategies that respond to evolving vaccine attitudes subject to budget constraints. We find that the efficiency of campaigns depends on both the network structure and the timing of the intervention. For early intervention, we demonstrate that targeting neutral individuals connected to anti-vaccine opinion adopters within their social networks can effectively limit the spread of negative influence. Moreover, we find that targeting agents that have the potential to propagate the positive influence in their neighbourhoods is significantly more effective than solely protecting the most vulnerable agents from negative influence. For late intervention, as large anti-vaccine communities begin to emerge, shielding bridging regions in small-world and regular lattice networks becomes a more effective containment strategy. However, this approach is less effective in scale-free and random networks due to the distinct clustering patterns observed there. We also find that controlling negative opinion diffusion becomes more challenging the longer the intervention is delayed. However, it can be controlled more efficiently with fewer resources in small-world and regular lattice networks than in others.

Reconstructing dynamics of complex systems from sparse, incomplete time series data is a challenging problem with applications in various domains. Here, we develop an iterative heuristic method to infer the underlying network structure and parameters governed by Ising dynamics from incomplete spin configurations based on sparse and small-sized samples. Our method iterates between imputing missing spin states given current coupling strengths and re-estimating couplings from completed spin state data. Central to our approach is the novel application of adaptive l_1 regularization on updating coupling strengths, which features an automatic adjustment of the regularization strength throughout the iterative inference process. By doing so, we aim at preventing over-fitting and enforcing the sparsity of couplings without access to ground truth parameters. We demonstrate that this approach accurately recovers parameters and imputes missing spins even with substantial missing data and short time series, providing improvements in the inference of Ising model parameters even for relatively small sample sizes.

Extant research on the effect of education system characteristics on school socio-economic segregation does not consider education as a complex system. This paper's contribution lies in using agent-based modelling to simulate the effect of the interaction between families' strategies for school selection and two education system characteristics: tracking - a system where students of different academic abilities are separated in different schools - and school accountability - the public availability of information on school quality. The model shows that school tracking and accountability tend to at the same time attenuate and increase school socio-economic segregation, but overall both policies tend to exacerbate segregation by eliciting competition for the best schools. The policy implications are: (i) tracking has a stronger exacerbating effect on segregation than accountability, (ii) the two polices interact to create compounding effects and (iii) by reducing residential segregation between families the segregating effect on schools of the two policies diminishes dramatically.

Inferring modelling parameters of dynamical processes from observational data is an important inverse problem in statistical physics. In this paper, instead of passively observing the dynamics for inference, we focus on strategically manipulating dynamics to generate data that gives more accurate estimators within fewer observations. For this purpose, we consider the inference problem rooted in the Ising model with two opposite external fields, assuming that the strength distribution of one of the fields (labelled as passive) is unknown and needs to be inferred. In contrast, the other field (labelled active) is strategically deployed to interact with the Ising dynamics in such a way as to improve the accuracy of estimates of inferring the opposing passive field. By comparing to benchmark cases, we first demonstrate that it is possible to accelerate the inference by strategically interacting with the Ising dynamics. We then apply series expansions to obtain an approximation of the optimized influence configurations in the high-temperature region. Furthermore, by using mean-field estimates, we also demonstrate the applicability of the method in a more general scenario where real-time tracking of the system is infeasible. Last, analysing the optimized influence profiles, we describe heuristics for manipulating the Ising dynamics for faster inference. For example, we show that agents targeted more strongly by the passive field should also be strongly targeted by the active one.

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. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.

En-route charging stations are essential to ensure the adoption of electric vehicles.However, careful planning is necessary due to high cost in infrastructure and potentially long waiting queues.Existing literature on the placement of charging stations largely disregards competition, sets prices to cover costs and/or disregards queues.In contrast, this work models competing station investors who aim to maximise expected profit, while electric vehicle drivers aim to minimise expected travel costs including queues.Following a game-theoretic approach, investors strategically decide station capacities, locations and charging unit power outputs as well as fees, taking into consideration building and operational costs.Given the complexity of the problem, the solution involves a combination of theoretical and algorithmic techniques to obtain subgame-perfect equilibria of investor and driver choices.Subgame-perfect equilibria are found to be at least 92.85% efficient, for reasonable fluctuations of problem parameters.Furthermore, it is found that charging prices can be up to approximately 5 times higher than marginal cost due to long charging times, and also that better charging technology may not necessarily benefit drivers in the near future.Finally, subsidies towards the purchase of charging units are shown to be beneficial for both drivers and investors, being able to generate up to 14.3% additional value than the cost of the subsidy.In contrast, subsidies on the energy price for stations are found to have small effect and can be abused by investors.

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. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.

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. We further explore differences in agent learning, discriminating between scenarios in which new agents learn from their parents (vertical transmission) versus scenarios in which they learn from their neighbors (oblique transmission), finding that vertical transmission results in faster convergence and generally higher communicability. Optimal languages can be formed when parental learning is dominant, but a small amount of neighbor learning is included. As a last point, we illustrate an exclusion effect leading to core-periphery networks in an adaptive networks setting when agents attempt to reconnect towards better communicators in the population.

Previous work has shown that communication between 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. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.

In this paper, we study the problem of opponent strategy inference from observations of information diffusion in voting dynamics on complex networks. We demonstrate that, by deploying resources of an active controller, it is possible to influence the information dynamics in such a way that opponent strategies can be more easily uncovered. To this end, we use the framework of maximum likelihood estimation and the Fisher information to construct confidence intervals for opponent strategy estimates. We then design heuristics for optimally deploying resources with the aim of minimizing the variance of estimates. In the first part of the paper, we focus on inferring an opponent strategy at a single node. Here, we derive optimal resource allocations, finding that, for low controller budget, resources should be focused on the inferred node and, for large budget, on the inferred nodes' neighbours. In the second part, we extend the setting to inferring opponent strategies over the entire network. We find that opponents are the harder to detect the more heterogeneous networks are, even with optimal targeting.

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. Agent dynamics are subject to peer influence and to the influence of two controllers. One of these controllers is treated as passive and we presume its influence is unknown. We then consider a scenario in which the other active controller attempts to infer the passive controller’s influence from observations. Moreover, we explore how the active controller can strategically deploy its own influence to manipulate the dynamics with the aim of accelerating the convergence of its estimates of the opponent. Along with benchmark cases we propose two heuristic algorithms for designing optimal influence allocations. We establish that the proposed algorithms accelerate the inference process by strategically interacting with the network dynamics. Investigating configurations in which optimal control is deployed. We first find that agents with higher degrees and larger opponent allocations are harder to predict. Second, even factoring in strategical allocations, opponent’s influence is typically the harder to predict the more degree-heterogeneous the social network.

The fight over setting the political agenda is one of the basic mechanisms of party competition of every democracy. However, this political game may have side effects in other aspects of the public debate. One aspect of general interest is how it may alter consensus formation processes among citizens, which may result in states of consensus, polarisation, or opinion fragmentation in the population. In this paper, we study the interrelated dynamics of two processes affecting opinion dynamics when multiple issues are debated. First, we model party competition via campaigning and its effect on the saliency or importance with which citizens perceive different political issues. Second, we consider a bounded–confidence model to describe the dynamics of citizens’ opinion and consensus formation. We find that the effects of party competition on consensus formation are rich and non-trivially dependent on the configuration of party positions in the political space. We illustrate that —as one would intuitively expect— there are party configurations that foster a paradigmatic state of polarisation for a wide range of model parameters. However, we also show that other party configurations have the opposite effect, and can facilitate reaching a consensus state that could otherwise not have been achieved. Our results illustrate the richness of possible outcomes of interrelations between party competition and consensus formation.

In this paper we present a game-theoretical model of rumour propagation on social networks. Agents face a choice between making investments at some cost to establish the truth about some underlying fact or copy the views of their network neighbours at no cost. Agents are also assumed to derive a benefit from knowledge about the truth. Considering rumour propagation at a fast time-scale and strategy adaptation at a slower time-scale, we present analysis of outcomes of the resulting evolutionary game. Depending on network structure and cost-benefit ratios, transitions between one and two-cluster solutions, either marked by the existence of only one type of strategy or coexistence of two strategies with low and high investments are found. We establish that clustering in the social network typically suppress the two-cluster solution, thus inhibiting the spread of high-investment strategies and leading to lower population-level awareness of the truth. Moreover, we also investigate the influence of free provision of additional high-quality information by stubborn agents. Counter-intuitively, we find that the presence of such agents encourages free riding – an effect that over-compensates the increased presence of higher quality information and has overall detrimental effects on the population.