<p>Recent research in network community detection has experienced a notable increase in interest, particularly regarding the application of resistance distance to elucidate complex organizational patterns, as well as the use of key node concepts to enhance community structures within intricate systems. Utilizing resistance distance to measure the effective resistance between nodes reveals community structures as discrete subsets characterized by high intra-connectivity and low inter-connectivity, beyond conventional distance measures. This approach advances graph theory and is significantly relevant in domains such as complex networks and big data science. This study introduces a novel community discovery algorithm, KeNRD (community detection based on key nodes and resistance distance), which integrates the resistance distance model with key node identification to assess proximity among network nodes, in conjunction with an enhanced density-based method for identifying community structures. The technique provides a thorough analysis that exceeds traditional methods by evaluating the contributions of both ordinary and important nodes within each community and incorporating the concept of noisy communities to enhance resilience. This method, grounded in rigorous experimentation with both real-world datasets and synthetic networks, alongside comparative analysis with state-of-the-art algorithms such as Copra, SLPA, Leiden, FluidC, Louvain, and others, underscores the superior efficacy of the proposed method in terms of modularity and NMI metrics, yielding enhanced insights into network dynamics. This study contributes to the academic discourse on community discovery by highlighting the significance of resistance distance in accurately and consistently revealing network topologies.</p>