User Interest Communities Influence Maximization in a Competitive Environment

In the field of social computing, influence-based propagation only studies the maximized propagation of a single piece of information. However, in the actual network environment, there are more than one piece of competing information spreading in the network, and the information will influence each other in the process of spreading. This paper focuses on the problem of competitive propagation of multiple similar information, which considers the influence of communities on information propagation, and establishes overlapping interest communities based on label propagation. Based on users' interests and preferences, the influence probability between nodes of different types of information is calculated, and combining the characteristics of the community structure, the influence calculation method of nodes is proposed. Specifically, aiming at the shortcomings of strong randomness in existing overlapping community detection methods that are based on label propagation, this paper proposes the User Interest Overlapping Community Detection Algorithm based on Label Propagation (UICDLP). Furthermore, when the seed node set of competition information is known, this paper proposes the Influence Maximization Algorithm of Node Avoidance (IMNA). Finally, the experimental results verified that the proposed algorithms are effective and feasible.