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With the rapid development of internet technology, open online social networks provide a broader platform for information spreading. While dissemination of information provides convenience for life, it also brings many problems such as security risks and public opinion orientation. Various negative, malicious and false information spread across regions, which seriously affect social harmony and national security. Therefore, this paper aims to minimize negative information such as online rumors that has attracted extensive attention. The most existing algorithms for blocking rumors have prevented the spread of rumors to some extent, but these algorithms are designed based on entire social networks, mainly focusing on the microstructure of the network, i.e. the pairwise relationship or similarity between nodes. The blocking effect of these algorithms may be unsatisfactory in some networks because of the sparse data in the microstructure.

An algorithm for minimizing the influence of dynamic rumor based on community structure is proposed in this paper. The algorithm first divides the network into communities, and integrates the influence of each node within communities and rumor influence probability to measure the influence of each node in the entire network, and then selects key nodes and bridge nodes in communities as blocked nodes. After that, a dynamic blocking strategy is adopted to improve the blocking effect of rumors.

Community structure is one of the most prominent features of networks. It reveals the organizational structure and functional components of a network from a mesoscopic level. The utilization of community structure can provide effective and rich information to solve the problem of data sparsity in the microstructure, thus effectively improve the blocking effect. Extensive experiments on two real-world data sets have validated that the proposed algorithm has superior performance than the baseline algorithms.

As an important research direction of social network analysis, rumor minimization has a profound effect on the harmony and stability of society and the development of social media. However, because the rumor spread has the characteristics of multiple propagation paths, fast propagation speed, wide propagation area and time-varying, it is a huge challenge to improve the effectiveness of the rumor blocking algorithm.

The purpose of this paper is to investigate the driving mechanisms for crack propagation regarding the related microstructures. Cracks in white etching layers have been found at the surface of submerged steel blades subjected to frictional sliding conditions.

In-situ monitoring revealed a fluctuation between mixed lubrication and hydrodynamic lubrication conditions. One lamella including a crack tip was prepared for transmission electron microscopy (TEM) using focused ion beam milling. Transmission electron microscope analysis was performed with the aim to understand the characteristics of the crack propagation, especially considering the influence of the microstructural configuration (grain refinement, carbides, martensite and ferrite grains).

The investigations have shown a grain-refined plastically deformed layer (friction martensite with grain sizes of < 100 nm) which influences the propagation direction of cracks introduced at the frictionally stressed surface. Thereby, the crack propagation is dominantly parallel to the margin of the grain-refined martensitic layer at the surface and the base material. Cracks were split into side cracks what mostly appears at present carbides. In this case, the crack propagation might strike through the carbide or separate it from the matrix due to the mechanical misfit.

For obtaining the results of this paper, a very special preparation of tribologically stressed samples was performed. Accordingly, specific findings of the crack propagation behavior under such conditions were achieved and are documented in the presented work. Moreover, the described crack propagation process is a combination of several mechanisms which occur in very limited region underneath the surface and are investigated by high-resolution TEM.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA.

In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network.

The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques.

This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

Important statistical variations are likely to appear in the propagation of surface plasmon polariton waves atop the surface of graphene sheets, degrading the expected performance of real-life THz applications. This paper aims to introduce an efficient numerical algorithm that is able to accurately and rapidly predict the influence of material-based uncertainties for diverse graphene configurations.

Initially, the surface conductivity of graphene is described at the far infrared spectrum and the uncertainties of its main parameters, namely, the chemical potential and the relaxation time, on the propagation properties of the surface waves are investigated, unveiling a considerable impact. Furthermore, the demanding two-dimensional material is numerically modeled as a surface boundary through a frequency-dependent finite-difference time-domain scheme, while a robust stochastic realization is accordingly developed.

The mean value and standard deviation of the propagating surface waves are extracted through a single-pass simulation in contrast to the laborious Monte Carlo technique, proving the accomplished high efficiency. Moreover, numerical results, including graphene’s surface current density and electric field distribution, indicate the notable precision, stability and convergence of the new graphene-based stochastic time-domain method in terms of the mean value and the order of magnitude of the standard deviation.

The combined uncertainties of the main parameters in graphene layers are modeled through a high-performance stochastic numerical algorithm, based on the finite-difference time-domain method. The significant accuracy of the numerical results, compared to the cumbersome Monte Carlo analysis, renders the featured technique a flexible computational tool that is able to enhance the design of graphene THz devices due to the uncertainty prediction.

In modern society, considering the multi-channel of public opinion information (public opinion) propagation and its strong influence on social development, it is necessary to study its propagation law and discuss the intervention strategy in online social networks (OSN).

First, a conceptual model of double-layer OSN was constructed according to their structural characteristics. Then, a cross-network propagation model of public opinion in double-layer OSN was proposed and discussed its spreading characteristics through numerical simulations. Finally, the control strategy of public opinion, especially the timing and intensity of intervention were discussed.

The results show that the double-layer OSN promotes the propagation of public opinion, and the propagation of public opinion in double-layer OSN has the characteristics of that in two single-layer OSN. Compared with the intervention intensity, the regulator should give the priority to the timing of intervention and try to intervene in the early stage of public opinion propagation.

This study may help the regulators to respond to the propagation of public opinion in OSN more actively and reasonably.

This research has a deep comprehension of the cross-network propagation rules of public opinion and manages the propagation of public opinion.

This paper aims to explore what factors influence the possibility of internal and external risk propagation in R&D networks and investigate how government intervention moderates the associations between the influencing factors and risk propagation.

The authors divided government intervention into directive and facilitative intervention and adopted an empirical research approach in this study. They collected 228 questionnaires from managers and R&D personnel participating in R&D projects in Shanghai and Jiangsu province through e-mail and in person. The data were used to carry out multiple regression analysis to test hypotheses.

The results show that the probability and consequence of risks positively affect the possibility of internal and external risk propagation; risk perception and transformation ability negatively influence the possibility of internal and external risk propagation; both directive and facilitative intervention weaken the relationship between the probability of risks and internal risk propagation when they are high than low the association between transformation ability and internal risk propagation is weaker when directive intervention is high than low, whereas facilitative intervention presents the insignificant moderation effect on the relationships between risk perception ability and internal and external risk propagation.

This study provides a distinctive theoretical perspective for risk conduction theory, government intervention theory and risk management. It also offered managers and the government a clear understanding of how to reduce or avoid risk propagation by leveraging directive and facilitative government intervention.

The purpose of this paper is twofold: first, to explore how the number of structural holes influences the possibility of risk propagation in R&D networks; and second, to investigate how the specific context of tie strength and common cognition moderate the association between structural holes and risk propagation.

This study focuses on how structural holes influence risk propagation under the specific context of relationship and cognitive dimension by drawing on social capital theory. Risk sharing and risk perception as mediating variables are employed in the proposed conceptual model. The authors issued questionnaires to managers and R&D personnel participating in R&D projects and collaboration in Shanghai and Jiangsu province through e-mail and face to face. The data were used to carry out multiple regression analysis to test hypotheses.

The results show that relationship between structural holes and risk propagation of R&D network is U-shaped. Risk perception and risk sharing partially mediate the relationship between structural holes and risk propagation. Tie strength significantly moderates the relationship between structural holes and risk sharing, but insignificantly moderates the association between structural holes and risk perception. Common cognition significantly moderates the associations between structural holes and risk sharing, and structural holes and risk perception, respectively.

This study provides a distinctive theoretical perspective for social capital and risk management. It also offers managers a clear understanding of how to reduce or to avoid risk propagation by jointly leveraging the number of structural holes, tie strength and common cognition.

Under the new media environment, while enjoying the convenience brought by the propagation of public opinion information (referred to as public opinion), learning the evolution process of public opinion and strengthening the governance of the spreading of public opinion are of great significance to promoting economic development and maintaining social stability as well as effectively resisting the negative impact of its propagation.

Thinking about the results of empirical research and bibliometric analysis, this paper focused on introducing key factors such as information content, social strengthening effects, etc., from both internal and external levels, dynamically designed public opinion spreading rules and netizens' state transition probability. Subsequently, simulation experiments were conducted to discuss the spreading law of public opinion in two types of online social networks and to identify the key factors which influencing its evolution process. Based on the experimental results, the governance strategies for the propagation of negative public opinion were proposed finally.

The results show that compared with other factors, the propagation of public opinion depends more on the attributes of the information content itself. For the propagation of negative public opinion, on the one hand, the regulators should adopt flexible guidance strategy to establish a public opinion supervision mechanism and autonomous system with universal participation. On the other hand, they still need to adopt rigid governance strategy, focusing on the governance timing and netizens with higher network status to forestall the wide-diffusion of public opinion.

The research conclusions put forward the enlightenment for the governance of public opinion in management practice, and also provided decision-making reference for the regulators to reasonably respond to the propagation of public opinion.

Our research proposed a research framework for the discussion of public opinion propagation process and had important practical guiding significance for the governance of public opinion propagation.

The purpose of this paper is to present the first results of tests where cracks lie in the tension field of a shear forced aluminium panel. The paper's main focus is on the crack propagation behavior and possible 3D‐effects caused by the bending of the plate. A simplified numerical approach is presented to confirm the observed phenomena.

Experiments have been performed to investigate the influence of buckling on accidental damages. A simplified numerical approach is presented and compared to the experimental results.

It can be shown that the crack propagates due to buckling of the plate. The principal stress of the neutral axis of the plate has significant influence on the crack propagation.

Investigations of stability problems and damage tolerance behavior of metallic structures have been realized but mainly separately. This paper shows that cracks propagate due to buckling and that both phenomena influence one another considering accidental damages. The paper presents the first experimental and numerical results of cracked aluminium panels subjected to cyclic shear load.