Today, social networks have found many applications in human daily life, so that identifying the behavior of members of this type of networks and the associations within them is of particular importance. Due to the structure and communication between members of social networks, some members within this type of network have more important roles than other members. In this study, a method for identifying more important associations was discussed. For this purpose, new features were introduced using network centralization features and then the importance of this type of features was investigated by Rough set theory. The experimental results showed that with increasing the number of popular nodes among an association introduced in this study and at the same time decreasing the amount of density, intermediate and proximity characteristics, the effect of the number of popular nodes on the association will remain more evident.

People's influence on their friends' personal opinions and decisions is an essential feature of social networks. Due to this, many businesses use social media to convince a small number of users in order to increase awareness and ultimately maximize sales to the maximum number of users. This issue is typically expressed as the influence maximization problem. This paper will identify the most Influential nodes in the social network during two phases. In the first phase, we offer a community detection approach based on the Node2Vec method to detect the potential communities. In the second phase, larger communities are chosen as candidate communities, and then the heuristicbased measurement approach is utilized to identify Influential nodes within candidate communities. Evaluations of the proposed method on three real datasets demonstrate the superiority of this method over other compared methods.

With the expansion of social networks, relationship between people has taken a new form. One of the important issues in social networks is social influence. Research on social influences and how information is disseminated in social networks, indicates that accepting or rejecting a new pattern by a person depends on the acceptance or rejection of the friends of that person. That is, because the people usually trust their friends more than other sources of advertising. As a result, many companies are focused on this type of advertisement which is called viral marketing. Given a large number of users in a social network, selecting the most Influential users as target users, through which a company can reach the highest expansion in the network with the lowest cost, is of great importance. In this paper, a new method for identifying the Influential nodes in social networks is proposed which is called MOSI (Multi-Objective algorithm based on Structured Information). The proposed method has two goals: "maximize profit" and "minimize similarity among selected users". The evaluation of the proposed method on real datasets indicates that our method has a greater expansion power in comparison with other similar methods.

Trust is one of the most important cornerstones in social networkschr discussions. Most of the times the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting to others. Meanwhile, users can impress each other and change their trusting patterns to other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks different user behavior in trusting other users. For this purpose, the three most important behavioral modes in users) trust are considered. In each of these modes behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and trust hardly. In this mode, the interactions that happened more recently have more value than those that happened in the past. In the next step, the way that the trust behavior of users spreads is the goal and innovation of this paper. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48. 14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37. 03 percent in behavior changing has much more reasonable results than usual models.

Nowadays, social networks have become a strong tool among researchers in addition to their social functions. This tool has many applications in identifying crimes, criminals and terrorists, solving epidemic problems, successful marketing and other topics in various fields. The researchers are using the influence maximization (IM) to achieve these goals. The task of maximization is to identify the Influential nodes that are known as the seed nodes. It is a strategy to achieve the maximum information diffusion or minimum epidemy with minimal cost. Since maximization is an NP-hard problem, researchers are looking for ways to reduce the complexity and acceptable identification accuracy by identifying Influential nodes. Therefore, to overcome the complexity and increase the identification accuracy, in this research a new method with activity-centrality combination is proposed. In this approach, to extract nodes by the centrality method a total constraint is constructed on the network graph in order to proceed to the local nodes extracted from the node activity analysis. The results of analyzing the activity of each node are combined with its centrality method score which ultimately leads to the identification of Influential nodes. The proposed method is compared with other methods such as PageRank and Closeness Centrality methods, and the evaluation results show that whilst having a lower complexity, the proposed method is better than both in terms of accuracy. In the future, the concepts of repetitive scoring can be used to further enhance the accuracy of the activity analysis phase.

In cognitive cyber-attacks, information dissemination analysis in online social networks is a very important issue. One of the main branches of information dissemination analysis is finding Influential nodes which this issue is also arisen in viral marketing as finding the Influential individuals. In this paper, while introducing and calculating two types of important nodes in information dissemination (reference and active nodes), a method for identifying these two important types of nodes in the dissemination of information in online social networks is presented and implemented based on the entropy theory. The proposed method in this paper is based on the evaluation of the entropy of the online social network graph generated from dissemination of information by removing the set of the most Influential nodes measured on the basis of the nodal-degree and the entropy of the nodes. The experiments of this paper show that the proposed algorithm is more capable of identifying the set of Influential nodes than the previous methods, in a way that the remaining set of nodes will have an adjustable homogeneity in influence measure and also presents a measure for determining the number of influencer nodes.

Many real-world networks, including biological networks, internet, information and social networks can be modeled by a complex network consisting of a large number of elements connected to each other. One of the important issues in complex networks is the evaluation of node importance because of its wide usage and great theoretical significance, such as in information diffusion, control of disease spreading, viral marketing and rumor dynamics. A fundamental issue is to identify a set of most Influential individuals who would maximize the influence spread of the network. In this paper, we propose a novel algorithm for identifying Influential nodes in complex networks with community structure without having to determine the number of seed nodes based on genetic algorithm. The proposed algorithm can identify Influential nodes with three methods at each stage (degree centrality, random and structural hole) in each community and measure the spread of influence again at each stage. This process continues until the end of the genetic algorithm, and at the last stage, the most Influential nodes are identified with maximum diffusion in each community. Our community-based influencers detection approach enables us to find more Influential nodes than those suggested by page-rank and other centrality measures. Furthermore, the proposed algorithm does not require determining the number of k initial active nodes.