Complexity最新文献

Influence maximization (IM) is a concept in social network analysis and data science that focuses on finding the most influential nodes (people, users, etc.) in a network to maximize the spread of information, behavior, or influence. IM studies have become more crucial due to the quick uptake of social media and networking technologies, which have revolutionized communication and information sharing. Using information from the Scopus database, this study conducts a thorough bibliometric analysis of the literature on instant messaging from 2006 to 2024 to investigate publishing trends, significant contributors, and developing themes. The three primary issues the study attempts to answer are finding the most productive journals, nations, and scholars in IM research; assessing the growth and influence of publications; and predicting future research trends. The results show that IM research is dominated by China and the US, with significant contributions from organizations like the Department of Computer Science and Microsoft Research Asia. The development of the field toward scalable algorithms and practical applications is highlighted by highly cited articles, such as Chen’s (2009) work on successful instant messaging. The investigation also shows the possibility of incorporating AI into future advancements and points out shortcomings in behaviorally informed techniques. This study offers a valuable summary of information management research for academics and professionals trying to understand this ever-evolving topic.

The emergence of hubs in scale-free networks plays a critical role in understanding dynamic complex networks such as social interactions, transportation networks, and biological processes. Given that real-world scale-free networks are dynamic and time based, a temporal-scale-free network (TSF network) is proposed in this paper. To predict the emergence of hubs, proposed a temporal graph convolutional neural network (T-GCN) that integrates graph convolutional networks (GCNs) for spatial feature extraction and long short-term memory (LSTM) networks for modeling temporal dynamics. Our framework effectively learns both the structural evolution and dynamic node interactions in scale-free networks, allowing accurate prediction of hub emergence. The proposed model is trained on synthetic and real-world datasets, demonstrating superior predictive accuracy compared to traditional methods. Our findings provide valuable insights into the mechanisms governing hub formation and offer a robust framework for forecasting influential nodes in evolving networks.

In this paper, we develop a periodic impulsive switching stage-structured model to investigate the population dynamics of species exhibiting hibernation behavior. The model incorporates stage structure (larvae and adults), birth pulses occurring exclusively in the active season, and impulsive harvesting events taking place immediately after hibernation. By combining switched dynamical systems with impulsive differential equations, we accurately capture the seasonal alternation between active and dormant states along with discrete reproductive and harvesting pulses. Using the Jury criterion, we establish sufficient conditions for the local asymptotic stability of both the population extinction periodic solution and the positive periodic solution. Furthermore, we identify an explicit extinction-survival threshold Γ and analyze how key parameters such as hibernation duration, harvesting rate, and birth pulse intensity govern population persistence. Numerical simulations not only validate the analytical results but also uncover complex nonlinear dynamics, including period-doubling bifurcations and chaotic oscillations, as the birth coefficient increases. These findings provide theoretical insights for wildlife conservation and sustainable harvesting strategies concerning hibernating species.

This paper presents the application of the Elzaki variational iteration method to solve the Black–Scholes model, which can be formulated as a heat-like partial differential equation with specified initial conditions. The Black–Scholes equation is fundamental in financial mathematics for option pricing, traditionally solved using numerical methods that are computationally intensive and prone to discretization errors. The proposed Elzaki variational iteration method combines the advantages of the Elzaki transform with the variational iteration technique to obtain exact analytical solutions. The Elzaki transform effectively converts the partial differential equation into a more tractable algebraic form, while the variational iteration method provides systematic solution construction. This hybrid approach yields solutions in the form of rapidly convergent infinite series. The analytical nature of our solutions offers significant computational advantages: option prices can be calculated within minutes with high precision, eliminating the need for time-consuming numerical iterations. The method provides exact formulas that can be evaluated to arbitrary accuracy, making it particularly valuable for real-time financial applications and high-frequency trading systems where speed and precision are critical. Numerical examples demonstrate the effectiveness and rapid convergence of the infinite series solutions across various parameter ranges. This work establishes the Elzaki variational iteration method as a powerful analytical tool for solving financial differential equations, offering superior efficiency compared to traditional numerical approaches.

This study introduces a novel fractional age-structured Susceptibles-Exposed-Infective-Hospitalized-Recovered-Adults (SEIHRA) model, designed to analyze measles transmission dynamics, particularly in younger populations. By incorporating age structure and an innovative inverse method, the model bridges mathematical rigor with empirical data. We examine equilibrium points, stability, and the basic reproduction number (R₀), while using the inverse method to estimate the time-dependent transmission rate β(t) from real-world outbreak data. Validated with Chinese measles data (1974–2022), the model captures temporal and age-specific trends, achieving an optimal fractional order of 0.94. Sensitivity analysis via the partial rank correlation coefficient (PRCC) technique highlights key parameters influencing R₀. Combining age structure and inverse methods, this work reveals age-specific transmission patterns and evaluates targeted vaccination strategies, offering critical insights for public health policies and global measles eradication efforts.

This work explores the challenges related to the 4-part harmony problem, addressing both the computational complexity of the search space and the benefits of integrating human teaching/learning processes into evolutionary problem-solving approaches. From a computational perspective, we analyze strategies to enhance algorithm efficiency, including parallelization, precomputation of fitness values, directed mutation, and adaptive directed mutation, which collectively reduce the time required to find solutions. Synthetic harmonic models are employed to validate these techniques. Complementing this, we investigate the role of human expertise, emphasizing the synergy between expert teaching and the learning processes of novice students. By examining how human teaching and learning paradigms can inspire innovative problem-solving techniques, we draw on the concept of evolutionary machine teaching, which reduces the search space, applied here to a standard harmonic model. Our findings highlight the potential of integrating computational advancements with methodologies driven by human learning. Specifically, the search space produced by Sharpmony students accounts for less than 1% of the total space. Using this approach, we have achieved a fourfold speedup over previous results of the same quality. Moreover, longer runs of the new approach have provided solutions with an average fitness of less than 1 error, considering the complete set of 50 rules and exceptions.

Cellular automata are powerful tools for simulating dynamic environments. Their ability to model complex systems where the environment actively influences outcomes makes them invaluable for studying phenomena such as wildfires, marine pollution, and population dynamics. However, traditional cellular automata are limited by discrete representations and rigid data structures, hindering their application in spatially complex scenarios. This paper introduces a generalized cellular automaton designed to overcome these challenges. By incorporating continuous space evolution and leveraging tensorial data structures, our model offers a more accurate, flexible, and computationally efficient framework for simulating real-world systems. This approach significantly simplifies the integration of geographical information into discrete simulations, expanding the potential of cellular automata in fields such as environmental science, population ecology, or theoretical physics. Moreover, our work contributes to a deeper understanding of tensorial representations and the concept of time using a computational approach.

RETRACTION: D. Yang, X. Ye, and B. Guo, “Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification,” Complexity, 2021, 5546338, https://doi.org/10.1155/2021/5546338.

The authors agree to the retraction.

Urban travel behavior in developing cities forms a complex system with nonlinear interactions among socioeconomic factors, land use patterns, and transportation infrastructure. This study examines these intricate dynamics in Rajshahi City Corporation (RCC), Bangladesh, using a multimodel approach to capture emergent properties of urban mobility. Analyzing data from 2286 households across six zones, we developed three interconnected models: Trip Production Model (TPM), Trip Attraction Model (TAM), and Household Kilometers Traveled Model (HKTM). The TPM showed that increasing household size by one unit boosts trip production by 1.537 times, while a one-unit increase in accessibility raises it by 1.930 times. Interestingly, the TAM revealed that higher accessibility can decrease trip attractions (coefficient: −1.412), indicating emergent congestion effects. The HKTM indicated that a one-unit improvement in road connectivity leads to an increase of 2.652 km in household travel. Our results demonstrate that socioeconomic and land use factors explain 75.1% of the variability in trip production, emphasizing the system’s complexity. The City Center, with the highest entropy index (0.80), attracted the most trips, whereas the Northern Fringe, despite a low entropy (0.52), generated the highest number of trips. These surprising findings highlight the nonlinear relationships in urban mobility and stress the importance of context-specific solutions to address urban transportation challenges. By applying complex systems theory, including concepts of self-organization and feedback loops, we provide a comprehensive framework for understanding and modeling urban transport dynamics in developing areas, offering valuable insights for adaptive policy-making amid rapid urban growth.

RETRACTION: C. Di, J. Peng, Y. Di, and S. Wu, “3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network,” Complexity 2021, no. 1 (2021): 6752120, https://doi.org/10.1155/2021/6752120.

The above article, published online on 25 May 2021 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by John Wiley & Sons Ltd.

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.