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Influence maximization problem is trying to identify a set of nodes by which the spread of influence, diseases or information is maximized. The optimization of influence by finding such a set is NP-hard problem and a key issue in analyzing complex networks. In this paper, a new greedy and hybrid approach based on a community detection algorithm and an MADM technique (TOPSIS) is proposed to cope with the problem, called, 'Greedy TOPSIS and Community-Based' (GTaCB) algorithm. The paper concisely introduces community detection and TOPSIS technique, then it presents the pseudo-code of the proposed algorithm. Afterwards, it compares the performance of the solution which is found by GTaCB with some well-known greedy algorithms, based on Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank as well as TOPSIS, from two aspects: diffusion quality and diffusion speed. In order to evaluate the performance of GTaCB, computational experiments on nine different types of real-world networks are provided. The tests are conducted via one of the renowned epidemic diffusion models, namely, Susceptible-Infected-Recovered (SIR) model. The simulations exhibit that in most of the cases the proposed algorithm significantly outperforms the others, chiefly as number of initial nodes or probability of infection increases.

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Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

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Summary An optimization approach is taken to locating the optimal set of initial contacts in a social network to maximize the number of total network members reached by a message. It is assumed that initial contacts are costly and that the number of initial contacts must be minimized simultaneously with maximizing the total number of network members contacted. A bi‐objective probabilistic integer programming model is developed that assumes that actors are heterogeneous in the probability that they will pass messages along their ego networks. Considering the complexity of solving the proposed model, it reformulated as a pure integer programming model. The algorithm is illustrated by the analysis of message passing in a short‐message system (texting) among university students. Copyright © 2015 John Wiley & Sons, Ltd.

PurposeMovies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?Design/methodology/approachIn the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.FindingsThe social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.Originality/valueThe novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

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