Graph embedding based ant colony optimization for negative influence propagation suppression under cost constraints

In the information spreading mechanism of social networks, the influence propagation of information sources often has different effects on different users. How to effectively suppress the negative effects is particularly important. In the case of unknown network propagation principle, this paper introduces the idea of swarm intelligence, which utilizes the positive feedback mechanism of ant colony to simulate the propagation of negative influence, and finds a set of high-value and low-cost suppression nodes. On this basis, the graph embedding technique is used to obtain the new relationships between nodes in the network, and the new relationships between the nodes are used as heuristic information for the ant colony algorithm. Experiments show that our algorithm can not only find the set of inhibitory nodes with limited cost, but also effectively limit the spread of negative influence in the network compared with other algorithms. The research of this paper can not only enrich the theoretical research results of influence maximization, but also play an important role in the analysis of network topology, as well as in the fields of epidemic prevention and control, rumor propagation and so on.