Technology advancements have enabled data collection from different platforms, such as the web and social media. A network consisting of nodes connected by edges is a typical approach to representing these data. The nodes represent the items in the networks, while the edges represent the interactions between the nodes. Community detection methods have been used extensively in analyzing these networks. However, community detection in evolving networks has been a significant challenge because of frequent network changes and the need for real-time analysis. Using static community detection methods for analyzing dynamic networks is not always applicable because static methods do not retain a network's history and cannot provide real-time information about the communities in the network. Existing incremental methods treat changes to the network as a sequence of edge additions and removals; however, in many real-world networks, changes occur with the simultaneous addition of a node and all its connecting edges. For the efficient processing of such large networks promptly, there is a need for an adaptive and analytical method that can process large networks without recomputing the entire network after its evolution and treat all the edges involved with a node equally. We proposed a node-centric community detection method that incrementally updates the community structure in the network using the already known structure to avoid recomputing the entire network from scratch and consequently achieve a high-quality community structure. The results from our experiments suggest that our approach is efficient for incremental community detection of node-centric evolving networks.