With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m -bonacci sequences to detect eavesdropping. Meanwhile, we encode m -bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding

By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

https://www.mdpi.com/2078-2489/8/4/118/pdf

A Survey on Information Diffusion in Online Social Networks: Models and Methods

In last 30 years, many metaheuristic algorithms have been developed to solve optimization problems. However, most existing metaheuristic algorithms have extra control parameters except the essential population size and stopping criterion. Considering different characteristics of different optimization problems, how to adjust these extra control parameters is a great challenge for these algorithms in solving different optimization problems. In order to address this challenge, a new metaheuristic algorithm called group teaching optimization algorithm (GTOA) is presented in this paper. The proposed GTOA is inspired by group teaching mechanism. To adapt group teaching to be suitable for using as an optimization technique, without loss of generality, four simple rules are first defined. Then a group teaching model is built under the guide of the four rules, which consists of teacher allocation phase, ability grouping phase, teacher phase and student phase. Note that GTOA needs only the essential population size and stopping criterion without extra control parameters, which has great potential to be used widely. GTOA is first examined over 28 well-known unconstrained benchmark problems and the optimization results are compared with nine state-of-the-art algorithms. Experimental results show the superior performance of the proposed GTOA for these problems in terms of solution quality, convergence speed and stability. Furthermore, GTOA is used to solve four constrained engineering design optimization problems in the real world. Simulation results demonstrate the proposed GTOA can find better solutions with faster speed compared with the reported optimizers.

Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.

/pdf/a-measure-of-individual-role-in-collective-dynamics-27uz5as74j.pdf

A measure of individual role in collective dynamics

Identification of influential nodes in complex networks is a significant problem.We propose a centrally measure to identify nodes location in complex networks.Proposed measure is applied to rank the influential nodes.Experiments show better results as compared to the state-of-the-art methods. Due to the rapid extension of social networks in recent years, a new potential has emerged for global spreading of messages and effective broadcasting of news. Identification of influential nodes within a network is now seen as a key factor for bringing this potential into action. k-shell is a measure for detection of node influence, and has already been used in some successful algorithms in this field. However, k-shell does not provide enough information about the topological positions of the nodes, and the present paper seeks to present a special hierarchical measure for dealing with this issue. Along with introduction of the above measure, it is shown that it can be used for detecting and ranking node influence. The experiments done on real-world and artificial networks demonstrate that the proposed approach can rank the influence of nodes more accurately than other approaches.

A hierarchical approach for influential node ranking in complex social networks

A challenging issue in viral marketing is to effectively identify a set of influential users. By sending the advertising messages to this set, one can reach out the largest area of the network. In this paper, we formulate the influence maximization problem as an optimization problem with cost functions as the influentiality of the nodes and the distance between them. Maximizing the distance between the seed nodes guarantees reaching to different parts of the network. We use gray wolf optimization algorithm to solve the problem. Our experimental results on three real-world networks show that proposed method outperforms state-of-the-art influence maximization algorithms. Furthermore, it has lower computational time than other meta-heuristic methods.

Identification of influential users in social network using gray wolf optimization algorithm

Identification of influential users for news and messages propagation constitutes one of the most important topics in analysis of social networks. The success of the spreading process in these networks depends on the mechanism for selection and specification of the influential users. Beside selection of influential users, the distances between selected users should be considered in this mechanism to ensure minimum overlap and maximum coverage of a wider area of the network. Simultaneous meeting of the two objectives may be contradictory. In this paper, we propose a new approach for selection of the set of influential users using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method that seeks to present a solution by considering the above two objectives. The simulation results over real-world and artificial networks demonstrate that the set selected by the proposed approach exhibits greater spread of influence than those selected by the traditional approaches.

Ahmad Zareie

Papers

A hierarchical approach for influential node ranking in complex social networks

Identification of influential users in social network using gray wolf optimization algorithm

Influence maximization in social networks based on TOPSIS

Identification of influential users in social networks based on users interest

Influential node ranking in social networks based on neighborhood diversity