Self-media, characterized by immediacy, interactivity, and decentralized dissemination, has emerged as a dominant force in information diffusion, significantly reshaping public risk perception and epidemic propagation dynamics during public health crises. While existing research has predominantly focused on empirical analyses of self-media’s impact on public emotions and behaviors, a theoretical framework capable of systematically elucidating the underlying mechanisms remains underdeveloped. To address this gap, this study develops a novel coupled awareness-epidemic spreading model that incorporates the influence of self-media. The model incorporates specialized self-media nodes to capture their enhanced role in awareness diffusion. It further introduces a feedback mechanism where the media heat quantified by the activity level of self-media nodes—intensifies the global information environment, thereby facilitating the spontaneous awakening of awareness among individuals even without local contact. Through the Microscopic Markov-Chain Approach, we analytically determine the epidemic threshold and characterize the system behavior. Numerical simulations confirm that the presence and activity of self-media suppress epidemic propagation and elevate the outbreak threshold by enhancing the efficiency of awareness diffusion. This study provides both theoretical foundations and practical insights for public opinion governance and risk intervention during public health emergencies in the self-media era.

Modern social media can facilitate the diffusion of epidemic-related information during pandemics, thereby enhancing individual epidemic awareness. However, current research places less emphasis on self-isolation behaviors stimulated by such awareness, which are crucial for long-term epidemic response. Thus, we propose a coupled information-epidemic spreading model that incorporates the impact of mass media and self-isolation behaviors. Using the Microscopic Markov Chain Approach, we analyze the model, determine the epidemic threshold, and investigate parameters contributing to intertwined dynamics. Experiments show that self-isolation effectively raises the epidemic threshold and reduces outbreak scope. Besides, stronger mass media diffusion enhances self-isolation's inhibitory effect on epidemic spread. There exists a meta-critical point in information diffusion impact; only when exceeding it does information diffusion increase the epidemic threshold, but mass media presence can eliminate this point. This research underscores the critical role of mass media and self- isolation in controlling epidemics, offering valuable insights for prevention strategies.

Effectively classifying anomalies in a multi-class setting holds significant importance in domains such as medical datasets, fraud detection, and anomaly detection. This task presents challenges that include efficient training on large datasets, accurate classification in imbalanced scenarios, and sensitivity to high imbalance ratios (IR). This paper introduces a novel approach, the Intuitionistic Fuzzy Twin Support Vector Machine-based Decision Tree (NDT-IFTSVM), aimed at addressing these issues. NDT-IFTSVM integrates IFTSVM and decision tree methodologies, offering an efficient solution for multi-class classification. The proposed algorithm constructs a decision tree comprised of a series of two-class IFTSVMs. To enhance balance and separability, the multi-class method iteratively divides into two sets based on distance between class centres and instance distribution. This recursive process continues until each subset exclusively contains a single class, facilitating effective classification. To handle highly imbalanced datasets, NDT-IFTSVM incorporates a rational weighting strategy. Additionally, we refine NDT-IFTSVM by introducing a regularization term that maximizes the margin between the bounding and proximal hyperplanes, mitigating the impact of noise and outliers. Finally, a coordinate descent system with shrinking by an active set is applied to reduce the computational complexity. Numerical evaluations employ the bootstrap technique with a 95% confidence interval and statistical tests to quantify the significance of performance improvements. Experimental results on 12 datasets demonstrate the efficacy of the proposed method, showcasing promising outcomes compared to other techniques documented in the literature.

Epidemic transmission and the associated awareness diffusion are fundamentally interactive. There has been a burgeoning interest in exploring the coupled epidemic-awareness dynamic. However, current research predominantly focuses on self-protection behavior stimulated by awareness, paying less attention to self-isolation behavior. Given the constraints of government-mandated quarantine measures, spontaneous self-isolation actions assume greater significance in the long-term response to epidemics. In response, we propose a coupled awareness-epidemic spreading model with the consideration of self-isolation behavior and subsequently employ a Micro Markov Chain Approach to analyze the model. Extensive experiments show that self-isolation behavior can effectively raise the epidemic threshold and reduce the final outbreak scale. Notably, in multiplex networks with positive inter-layer correlation, the inhibitory effect is the greatest. Moreover, there exists a metacritical point, only when the awareness diffusion probability exceeds the critical value of this point, the epidemic threshold will increase with the increase of awareness diffusion probability. In addition, the growth of the average degree of the virtual-contact layer can reduce the value of this metacritical point. This research emphasizes the significant role of self-isolation behavior in curbing epidemic transmission, providing valuable perspectives for epidemic prevention through the interplay of awareness and epidemic spreading.

As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information-epidemic dynamics, while accounting for the promotional effect of mass media in information dissemination, is of significant practical relevance. Nonetheless, in the extant research, scholars predominantly employ an assumption that mass media broadcast to all individuals equally within the network: this assumption overlooks the practical constraint imposed by the substantial social resources required to accomplish such comprehensive promotion. In response, this study introduces a coupled information-epidemic spreading model with mass media that can selectively target and disseminate information to a specific proportion of high-degree nodes. We employed a microscopic Markov chain methodology to scrutinize our model, and we examined the influence of the various model parameters on the dynamic process. The findings of this study reveal that mass media broadcasts directed towards high-degree nodes within the information spreading layer can substantially reduce the infection density of the epidemic, and raise the spreading threshold of the epidemic. Additionally, as the mass media broadcast proportion increases, the suppression effect on the disease becomes stronger. Moreover, with a constant broadcast proportion, the suppression effect of mass media promotion on epidemic spreading within the model is more pronounced in a multiplex network with a negative interlayer degree correlation, compared to scenarios with positive or absent interlayer degree correlation.

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying the influential latent edges (that is, the edges that do not originally exist) which, if added to the original network, can promote the cooperative susceptible-infected-recovered (co-SIR) dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations.

随着大数据技术的发展,人类社会活动积累了海量的数据.通过这些数据,对人类活动的时空特性进行分析发现人类活动的时间间隔表现出近似幂律分布的特点,并且人类活动的幂律指数随用户活跃性的提高而增长.为了研究这种幂律现象的成因,提出了一种改进的时间重定标算法,选取个体两个相继行为发生的时间间隔内,同一时间其他个体所发生的行为总数与该个体平均时间间隔的乘积作为新的时间度量.新的算法可以消除用户活跃性的周期和波动对实验结果的影响,并且兼顾个体活跃性的作用.最后以该度量重新对个体行为的时间间隔分布进行了分析.实验结果表明,在新的时间度量下,其仍然表现出幂律的分布特点.说明人类活动的幂律特性的成因与用户活跃性的周期和波动无关,而是由个体内禀的特性引起.

We investigate the effects of self-protection awareness on the spread of disease from the aspect of resource allocation behavior in populations. To this end, a resource-based epidemiological model and a self-awareness-based resource allocation model in complex networks are proposed, respectively. First of all, we study the coupled disease-awareness dynamics in complex networks with fixed degree heterogeneity. Through extensive Monte Carlo simulations, we find that overall the self-awareness inhibits the spread of disease. More importantly, the influence of the self-awareness on the spreading dynamics can be divided into three phases. In phase I, the self-awareness is relatively small and the outbreak of the epidemic can not be suppressed effectively. While, in phase II, the epidemic size is significantly reduced. Finally, in phase III, there is a sufficiently large value of self-awareness, the disease cannot outbreak anymore. Further, we study the impact of degree heterogeneity on the coupled disease-awareness dynamics and find that the network heterogeneity plays the role of “double-edged sword” in that it can either suppress or promote the epidemic spreading. Specifically, when the basic infection rate is relatively small, it promotes the spread of disease under the condition that there is a relatively small self-awareness. While, when the basic infection rate is relatively large, it inhibits the outbreak of epidemic at a relatively small self-awareness; in turn, it promotes the outbreak of epidemic at a relatively large self-awareness.

Promoting some typical spreading dynamics, for instance, the spreading of information, commercial message, vaccination guidance, innovation, and political movement, can bring benefits to all aspects of the socio-economic systems. In this study, we propose a strategy for promoting the spreading of the susceptible-infected-recovered model, which is widely applied to describe these common spreading dynamics in real life. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way of promoting the spreading of the susceptible-infected-recovered model by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading models.

Real-world systems, ranging from social to infrastructural, can be abstracted into complex networks. Promoting the spreading of some typical information (for instance, the commercial message, vaccination guidance, innovation, and political movement) on these networked systems can bring benefits to all aspects of society. In this study, we propose an effective edge-based approach for promoting the spreading of information on complex networks. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the information spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way to promote the spreading of information by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading dynamics.

Numerous real-world systems, for instance, communication platforms and transportation systems, can be abstracted into complex networks. Containing spreading dynamics (e.g. epidemic transmission and misinformation propagation) in networked systems is a hot topic on multiple fronts. Most of the previous strategies are based on the immunization of nodes. However, sometimes, these node-based strategies can be impractical. For instance, in train transportation networks, it is excessive to isolate train stations for flu prevention. On the contrary, temporarily suspending some connections between stations is more acceptable. Thus, we pay attention to the edge-based containment strategy. In this study, we develop a theoretical framework to find the optimal edge for containing the spread of the susceptible-infected-susceptible model on complex networks. To be specific, by performing a perturbation method to the discrete-Markovian-chain equations of the SIS model, we derive a formula that approximately provides the decremental outbreak size after the deactivation of a certain edge in the network. Then, we determine the optimal edge by simply choosing the one with the largest decremental outbreak size. Note that our proposed theoretical framework incorporates the information of both network structure and spreading dynamics. Finally, we test the performance of our method by extensive numerical simulations. Results demonstrate that our strategy always outperforms other strategies that are based only on structural properties (degree or edge betweenness centrality). The theoretical framework in this study can be extended to other spreading models and offers inspiration for further investigations on edge-based immunization strategies.

School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, School of Economic Information Engineering, Chengdu 611130, China Aba Teachers University, Aba 623002, China Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China Key Laboratory of Intelligent Manufacturing Technology (Ministry of Education), Shantou University, Shantou 515063, China

K-means算法具有简单易于理解的特征,广泛运用于聚类过程中,但是其初始聚类中心是随机确定的,这样极容易导致聚类结果的稳定性很差.针对传统K-means算法对于初始聚类中心选择的敏感性及最大最小距离法容易选取离散点的不足,提出了一种新的聚类中心选择评判函数,依次考察每个点的函数值,选取当前函数值最大的点作为新的聚类中心,直到满足事先确定的聚类中心数.新聚类中心评判函数既可以保证新中心点周围是紧凑的,又可以保证远离其他中心点.最后将该算法运应用于文本聚类之中,根据准确率、召回率及F度量值来衡量算法的聚类质量.实验结果表明,该算法相对于传统算法和最大最小距离算法,准确率更高,聚类质量更好,较适合于文本聚类.

Novel data has been leveraged to estimate the socioeconomic status in a timely manner, however, direct comparison on the use of social relations and talent movements remains rare. In this letter, we estimate the regional economic status based on the structural features of two networks. One is the online information flow network built on the following relations on social media, and the other is the offline talent mobility network built on the anonymized resume data of job seekers with higher education. We find that while the structural features of both networks are relevant to the economic status, the talent mobility network in a relatively smaller size exhibits a stronger predictive power for the gross domestic product (GDP). In particular, a composite index of structural features can explain up to about 84% of the variance in GDP. The result suggests that future socioeconomic studies should pay more attention to the cost-effective talent mobility data. Copyright (C) EPLA, 2019

The numerous expanding online social networks offer fast channels for misinformation spreading, which could have a serious impact on socioeconomic systems. Researchers across multiple areas have paid attention to this issue with a view of addressing it. However, no systematical theoretical study has been performed to date on observing misinformation spreading on correlated multiplex networks. In this study, we propose a multiplex network-based misinformation spreading model, considering the fact that each individual can obtain misinformation from multiple platforms. Subsequently, we develop a heterogeneous edge-based compartmental theory to comprehend the spreading dynamics of our proposed model. In addition, we establish an analytical method based on stability analysis to obtain the misinformation outbreak threshold. On the basis of these theories, we finally analyze the influence of different dynamical and structural parameters on the misinformation spreading dynamics. Results show that the misinformation outbreak size R(∞) grows continuously with the effective transmission probability β once β exceeds a certain value, that is, the outbreak threshold βc. Large average degrees, strong degree heterogeneity, or positive interlayer correlation will reduce βc, accelerating the outbreak of misinformation. Besides, increasing the degree heterogeneity or a more positive interlayer correlation will enlarge (reduce) R(∞) for small (large) values of β. Our systematic theoretical analysis results agree well with the numerical simulation results. Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks and thereby pave the way to address this serious issue.

Understanding human behavior is becoming a key issue in many areas of research. Based on the data set of product reviews from Amazon., this paper gives an intuitive understanding of human interest evolution through the study of people's rating behavior on video games. Results disclose the increasing inequality in active users' interests distribution., and reveal the time-invariant power law distribution of active users'interest span. All these findings have significant application value in both science and commerce.

World development indicators can reflect the development situation of countries intuitively, while the calculations of them are time and resource consuming. This paper reveals the strong correlations among these indicators, which has significant application value in simplification of development indicators systems and prediction of unknown indicators. The results help to meet the demand of real-time economic and political decision-making and save resources.

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α. By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α, which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1<α≤3, as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α>3, the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.

The degree-degree correlation is important in understanding the structural organization of a network and dynamics upon a network. Such correlation is usually measured by the assortativity coefficient r, with natural bounds r∈[-1,1]. For scale-free networks with power-law degree distribution p(k)∼k-γ, we analytically obtain the lower bound of assortativity coefficient in the limit of large network size, which is not -1 but dependent on the power-law exponent γ. This work challenges the validation of the assortativity coefficient in heterogeneous networks, suggesting that one cannot judge whether a network is positively or negatively correlated just by looking at its assortativity coefficient alone.