Social influence has been widely discussed in various disciplines due to its important sociological significance. However, the dynamics of social influence in signed networks have nonetheless received fairly little attention. In this article, we propose a generalized Pólya urn model that considers the effect of negative relationships and is capable of comprehending the specific mechanisms of homophily and xenophobia in the dynamics. Based on the mathematical deduction, we find that the signed network guides social influence in a trend toward equality. Simulation shows that a higher effect or larger proportion of negative relationships may break the self-reinforcement and make the market more equal. The collective dynamics in the signed network are more predictable but generate path dependence. We also find that a balanced structure has no impact on the average market share but is helpful in removing path dependence and promoting system stability.

Online video is the most popular Internet application. As the throughput would frequently change under different network conditions, it is important to adaptively select the proper bitrate and improve user’s quality of experience. In this paper, we propose a new DRL-based rate adaption algorithm for video streaming, which holistically captures user’s preference of video contents, network throughput and buffer occupancy, and select the proper bitrate for video to improve the QoE. Specifically, we use 3D Convolutional neural (C3D) network to learn the spatio-temporal features, and implement the semantic analysis of videos. We also apply the Term Frequency-Inverse Document Frequency (TF-IDF) method to analyze the user’s preference of different scene types, according to its viewing history. The dynamic adaptive streaming is formulated as a Markov Decision Process (MDP) problem, and use the Actor-Critic (A3C) algorithm to dynamically choose the optimal bitrate. As corroborated by simulations, our algorithm can accurately obtain the user’s preference, keep the bitrate allocation consistent with the user’s preference, and maintain video quality. Compared with the state-of-the-art Pensieve algorithm, our algorithm improves the average QoE by at least 12.5%. It also has a significant improvement over other baseline methods.

In the social Internet of Things, social networks can be built among smart objects or between smart objects and people just like human beings. One of the factors that determines the effect and efficiency of service matching in SIoT is the structure of social networks. In this paper, we exploit the theory of structural balance in signed networks to optimize SIoT network structures to provide a friendly and stable environment as well as a solid foundation for service matching of social Internet of Things. Next, besides being friends or enemies, which are traditional relationships of structural balance or structural changes in signed networks, in our research, we introduce the ambiguous relationship which is the certain state between the hostile and the friendly status and we discuss the meaning and significance of ambiguous relationship in dynamical changes of structural balance in SIoT networks. Based on previous studies, we apply an enhanced objective function and a modified approach concerning the ambiguous relationship towards the dynamical change process. Experiments show that our approach is more effective and efficient than former studies in optimizing dynamical evolution of structural balance in signed networks of SIoT.

Structural balance in fully signed networks consisting of both individual attributes and social interactions has great importance on social computing, but its sociological or physical mechanisms still need further exploration, and its measurement is still lacking. In this article, we discuss the new mechanism of balance theory and design a new energy function to measure the global structural balance. An optimization algorithm that minimizes the energy function to detect the communities in structural balance is proposed as well, and experiment results show the excellent effectiveness and efficiency of our proposed method. An application on the investigated data from Shenzhen, China, is also provided to explain the social significance of structural balance.

Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.

Research on network structural balance has been of great concern to scholars from diverse fields. In this paper, a two-step approach is proposed for the first time to address the network structural balance problem. The proposed approach involves evolutionary multiobjective optimization, followed by model selection. In the first step, an improved version of the multiobjective discrete particle swarm optimization framework developed in our previous work is suggested. The suggested framework is then employed to implement network multiresolution clustering. In the second step, a problem-specific model selection strategy is devised to select the best Pareto solution (PS) from the Pareto front produced by the first step. The best PS is then decoded into the corresponding network community structure. Based on the discovered community structure, imbalanced edges are determined. Afterward, imbalanced edges are flipped so as to make the network structurally balanced. Extensive experiments on synthetic and real-world signed networks demonstrate the effectiveness of the proposed approach.

Although differentiated services (DiffServ) networks have been well discussed in the past several years, a conventional Active Queue Management (AQM) algorithm still cannot provide low-complexity and cost-effective differentiated bandwidth allocation in DiffServ. In this paper, a novel AQM scheme called CHOKeR is designed to protect TCP flows effectively. We adopt a method from CHOKeW to draw multiple packets randomly from the output buffer. CHOKeR enhances the drawing factor by using a multistep increase and single-step decrease (MISD) mechanism. In order to explain the features of CHOKeR, an analytical model is used, followed by extensive simulations to evaluate the performance of CHOKeR. The analytical model and simulation results demonstrate that CHOKeR achieves proportional bandwidth allocation between different priority levels, fairness guarantee among equal priority flows, and protection of TCP against high-speed unresponsive flows when network congestion occurs.

In this paper, a mutation with cycle probability is designed by simulating the evolutionary rule of the earth creature, and a genetic algorithm based on the cycle mutation, presents the ability in improving search efficiency and overcoming premature to some extent. To further improve performance of the algorithm, the selection is mended according to the phenomena that optimum individual always plays a major role, and an improved cycle mutation genetic algorithm is proposed. The experiment results on the benchmark functions optimization show that exploration and exploitation of this algorithm is better than some well-known evolution algorithms and it is not sensitive to the initial population distribution.

Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

In this study, we introduce two improved assessment metrics of multiobjective optimizers, Nondominated Ratio and Spacing Distribution, and analyze their rationality and validity. Based on the concept of Immunodominance and Antibody Clonal Selection Theory, a novel multiobjective optimization algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA), is put forward. The simulation comparisons between IDCMA and the Strength Pareto Evolutionary Algorithm show that IDCMA has the best performance in popular metrics such as Spacing, Coverage of Two Sets and the two new metrics presented in this paper when low-dimensional multiobjective problems are concerned. The statistical results of the four metrics also show that Spacing Distribution conquers some limitations of Spacing triumphantly, and Nondominated Ratio conquers the limitation of Coverage of Two Sets that only compared between two sets.

Based on the Antibody Clonal Selection Theory of immunology, we put forward a novel clonal selection algorithm for multiuser detection in Code-division Multiple-access Systems. By using the clonal selection operator, the new algorithm can carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. After discussing the main characters of the new algorithm, especially the convergence and complexity, the performance of the proposed receiver, named by CAMUD, is evaluated via computer simulations and compared to that of other suboptimal schemes as well as to that of Optimal Multiuser detector (OMD) and conventional detector in CDMA systems over Multi-Path Channels. When compared with the OMD scheme, the CAMUD is capable of reducing the computational complexity significantly. On the other hand, when compared with standard genetic algorithm and improved genetic algorithm, theoretical analysis and Monte Carlo simulations show that the CAMUD with same complexity has optimal performance in eliminating MAI and "near-far" resistance. The simulations also show that the CAMUD performs quite well even when the number of active users and the length of the transmitted packet are considerably large.