Recognizing the most effective' propagators' in a network is a critical step toward maximizing the use of prevailing resources and ensuring that information is spread more effectively. Spreading is a term that encompasses a wide range of significant societal actions. Understanding how wrong information spreads across a network of social contacts is critical for finding practical approaches to slow or speed up information dissemination spread. Indeed, people are connected in society based on how they connect. The wide variety of the resulting network has a significant impact on the efficiency and speed with which information spreads. The most connected persons are seen as essential participants in networks with a broad degree distribution as they are responsible for the enormous scale of the course of infection. Furthermore, in social network theory, the value of a node for spreading is frequently linked to its betweenness centrality, which is a measurement of how many shortest paths pass through this node and is thought to define who has more significant 'interpersonal influence' on others. One of the areas of research in network evidence mining is identifying the influential nodes. Many closeness centralities used to assess node influence abilities struggle to balance accuracy and temporal complication. One of the research areas in network mining is identifying influential nodes. Because of the enormous scaled data and network sizes and the regularly changing behaviors of contemporary topologies, identifying influential nodes in multifaceted networks is difficult. Identifying essential nodes in compliant networks is critical in a variety of application scenarios, such as the spread of illness and immunization, disinfection and software virus infection, and greater product awareness and rumour destruction. Even though several ways to address the issues have been presented, most relevant research has focused on only a few specific areas of the problem. In this research, we conducted a brief review of recently published studies to identify various approaches that are useful in identifying prominent nodes in a complex network that are primarily responsible for the transmission of incorrect or correct information.