In social networks groups play a crucial role and making decisions based on majority consensus. Which Influencer nodes should we select if our goal is to broadcast a subject in a target group and increase the number of active nodes in this group? Here, we study a new Influence Maximization (IM) problem that focuses on individuals in a target group who are activated by some relevant topic or information. Target Group Influence Maximization (TGIM) aims to select k Influencer nodes in such a way that the number of activated nodes in the target group is maximized. In this paper, we study TGIM and focus on activating the majority of nodes in the target group. We propose an algorithm named Reinforcement Learning for Target Group (RLTG) based on the analysis of the Influence of nodes on the target group. The algorithm uses the reinforcement learning approach to learn the optimal path from each target node to some candidate Influencers. The experimental results indicate that the recommended approach outperforms known methods.

Nowadays, much attention has been devoted to the issues of social networks and social Influence. Social Influence examines the user's behavioral changes under the Influence of their neighbors. The issue of Influence Maximization is to find a subset of influential nodes that can maximize propagation in the network. The selection of people is very important and is the major aim of the studies. Hence, the current study aims to investigate the Maximization of Influence in signed social networks since in the psychology of society, negative opinions are superior to positive ones. The criteria considered for measuring Influence and methods to increase it by identifying influential people are examined. The proposed solution of this paper is based on the label propagation algorithm. The algorithms used for maximizing Influence in signed social networks namely a greedy algorithm and an innovative algorithm are outlined in the second section. To implement the algorithms and simulate the transfer of users' opinions in the graph network, the independent cascade propagation model is used. The proposed algorithm shows better performance and results compared to other algorithms and has less computational overhead since it finds primary nodes by detecting dense parts and not randomly. The significant novelty of the paper lies in the heart of the accuracy and authenticity of the proposed model in maximizing Influence in signed social networks.

POPULARITY OF ONLINE SOCIAL NETWORK SERVICES MAKES IT A SUITABLE PLATFORM FOR RAPID INFORMATION DIFFUSION RANGING FROM POSITIVE TO NEGATIVES INFORMATION. ALTHOUGH THE POSITIVE DIFFUSED INFORMATION MAY WELCOMED BY PEOPLE, THE NEGATIVE INFORMATION SUCH AS RUMOR, HATE AND MISINFORMATION CONTENT SHOULD BE BLOCKED. HOWEVER, BLOCKING INAPPROPRIATE, UNWANTED AND CONTAMINATION DIFFUSION ARE NOT TRIVIAL. IN PARTICULAR, IN THIS PAPER, WE STUDY THE NOTION OF COMPETING NEGATIVE AND POSITIVE CAMPAIGNS IN A SOCIAL NETWORK BY ADDRESSING THE Influence BLOCKING Maximization (IBM) PROBLEM TO MINIMIZE THE BAD EFFECT OF MISINFORMATION. IBM PROBLEM CAN BE DEFINED AS FINDING A SUBSET OF NODES TO PROMOTE THE POSITIVE Influence UNDER MULTICAMPAIGN INDEPENDENT CASCADE MODEL AS DIFFUSION MODEL TO MINIMIZE THE NUMBER OF NODES THAT ADOPT THE NEGATIVE Influence AT THE END OF BOTH PROPAGATION PROCESSES. IN THIS REGARD, WE PROPOSED A COMMUNITY BASED ALGORITHM CALLED FC_IBM ALGORITHM USING FUZZY CLUSTERING AND CENTRALITY MEASURES FOR FINDING A GOOD CANDIDATE SUBSET OF NODES FOR DIFFUSION OF POSITIVE INFORMATION IN ORDER TO MINIMIZING THE IBM PROBLEM. THE EXPERIMENTAL RESULTS ON WELL-KNOWN NETWORK DATASETS SHOWED THAT THE PROPOSED ALGORITHM NOT ONLY OUTPERFORMS THE BASELINE ALGORITHMS WITH RESPECT TO EFFICIENCY BUT ALSO WITH RESPECT TO THE FINAL NUMBER OF POSITIVE NODES.

In parallel with the development of online social networks, the number of active users in these media is increased, which mainly use these media as a tool to share their opinions and obtaining information. Propagation of Influence on social networks arises from a common social behavior called "mouth-to-mouth" diffusion among society members. The Influence Maximization (IM) problem aims to select a minimum set of users in a social network to maximize the spread of Influence. In this paper, we propose a method in order to solve the IM problem on social media that uses the network embedding concept to learn the feature vectors of nodes. In the first step of the proposed method, we extract a structural feature vector for each node by network embedding. Afterward, according to the similarity between the vectors, the seed set of influential nodes is selected in the second step. The investigation of the results obtained from applying the proposed method on the real datasets indicates its significant advantage against its alternatives. Specifically, the two properties of being submodular and monotonic in the proposed method, which lead to an optimal solution with the ratio of approximation, make this method considered a tool with high potential in order to address the IM problem.