Crowd psychology plays an important role in determining the kind of activities that a person performs. In reality in a social network, crowd influence has been observed and it cannot be ignored when considering information diffusion problems. In this paper, we model crowd influence as a hyperedge $e=(H_e,\;v)$ with weight $0\leq P_e\leq 1$ , where $H_e$ is the head node set and $v$ is the tail node, means $v$ will be activated by $H_e$ with probability $P_e$ only after each node in $H_e$ is activated. Then, the Social Influence Maximization Problem in Hypergraph (SIMPH) aims to select $k$ initially-influenced seed users in a directed hypergraph $G=(V,\;E,\;P)$ . The objective is to maximize the expected number of eventually-influenced users. We show that SIMPH is NP-hard and the objective function is neither submodular nor supermodular. We develop a lower bound and an upper bound that are submodular. We prove maximizing these two bounds are NP-hard under IC model. Then we present a D-SSA algorithm for general weighted social influence maximization problem preserving ( $1-1/e-\in$ )-approximation. We formulate a sandwich approximation framework, which preserves a theoretical analysis result. Finally, we evaluate our algorithm on real world data sets. The results show the effectiveness and the efficiency of the proposed algorithm

In recent years, online social media has flourished, and a large amount of information has spread through social platforms, changing the way in which people access information. The authenticity of information content is weakened, and all kinds of misinformation rely on social media to spread rapidly. Network space governance and providing a trusted network environment are of critical significance. In this article, we study a novel problem called activity minimization of misinformation influence (AMMI) problem that blocks a node set from the network such that the total amount of misinformation interaction between nodes (TAMIN) is minimized. That is to say, the AMMI problem is to select K nodes from a given social network G to block so that the TAMIN is the smallest. We prove that the objective function is neither submodular nor supermodular and propose a heuristic greedy algorithm (HGA) to select top K nodes for removal. Furthermore, in order to evaluate our proposed method, extensive experiments have been carried out on three real-world networks. The experimental results demonstrate that our proposed method outperforms comparison approaches.

Group plays an important role in social society. Much of the world's decision or work is done by groups and teams. A group's decision should be made based on most of the members in the group that reach agreement on a concerned topic. If we want to spread a topic and maximize the total number of activated groups in a social network, which seed users should we choose. In this article, we will study a new influence maximization (IM) problem which focuses on the number of groups activated by some concerned topic or information. A group is said to be activated if β percent of users in this group are activated. Group IM (GIM) aims to select k seed users such that the number of eventually activated groups is maximized. We first analyze the complexity and approximability of GIM, which is NP-hard, and the objective function presented in this article is proven to be neither submodular nor supermodular. We develop an upper bound problem and a lower bound problem whose objective functions are submodular. Then, an algorithm based on group coverage will be proposed, and the Sandwich framework is formulated with theoretical analysis to solve GIM. Our experiments verify the effectiveness of our method, as well as the advantage of our method against the other heuristic methods.

An echo chamber effect describes the situation in which opinions are amplified by communication and repetition inside a relatively closed social system. In this article, we will detect the echo chamber effect in real-world data set and measure this effect during the information diffusion process. Any user will be influenced by its neighbors or echo chamber effect. Also, we assume that these activation events from each activated neighbor and from echo chamber effect are independent. In this article, we detect and model the echo chamber effect for the first time. Then, the influence maximization with echo chamber (IMEC) problem aims to select $k$ users to propagate information such that the expected number of activated users is maximized. We formulate this problem using a graph model and analyze the NP-hardness. Second, the objective of IMEC as a set function is proved to be neither submodular nor supermodular. Then, an improved greedy algorithm is proposed, which is combined metaheuristic strategies. Finally, experimental results show that our algorithm is effective in detecting echo chamber effect and efficiency in selecting seed nodes.