The paper introduces *cytoHubba*, a novel Cytoscape plugin designed to rank nodes in biological networks based on their network features. *CytoHubba* offers 11 topological analysis methods, including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality, and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress). Among these, the Maximal Clique Centrality (MCC) method outperforms others in predicting essential proteins from the yeast protein-protein interaction (PPI) network. The plugin provides a user-friendly interface for exploring important nodes and sub-networks, and it can be integrated with other Cytoscape plugins for comprehensive network analysis. The performance of *cytoHubba* is validated through its ability to handle networks of varying sizes efficiently, and it has been widely used in various biological studies, including cancer metabolic networks, innate immune networks, and complex biofilm communities. The plugin is freely available and has been downloaded over 6,700 times since its release in 2010.The paper introduces *cytoHubba*, a novel Cytoscape plugin designed to rank nodes in biological networks based on their network features. *CytoHubba* offers 11 topological analysis methods, including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality, and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress). Among these, the Maximal Clique Centrality (MCC) method outperforms others in predicting essential proteins from the yeast protein-protein interaction (PPI) network. The plugin provides a user-friendly interface for exploring important nodes and sub-networks, and it can be integrated with other Cytoscape plugins for comprehensive network analysis. The performance of *cytoHubba* is validated through its ability to handle networks of varying sizes efficiently, and it has been widely used in various biological studies, including cancer metabolic networks, innate immune networks, and complex biofilm communities. The plugin is freely available and has been downloaded over 6,700 times since its release in 2010.