CytoHubba is a Cytoscape plugin that identifies hub objects and sub-networks in complex interactome data. It provides 11 topological analysis methods for ranking nodes in a network, including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality, and six centralities based on shortest paths. Among these, the Maximum Neighborhood Component (MCC) method performs best in predicting essential proteins from the yeast PPI network. The plugin offers a user-friendly interface for exploring important nodes in biological networks and allows researchers to combine it with other plugins for novel analysis schemes. It computes all eleven methods in one go and provides tools for retrieving sub-networks and analyzing network topological features. CytoHubba has been downloaded over 6,700 times since 2010 and is widely used for analyzing various biological networks, including cancer metabolic networks, innate immune networks, and complex biofilm communities. CytoHubba's performance was validated by predicting essential proteins in the yeast PPI network. The results showed that MCC outperformed other methods in identifying essential proteins, particularly in both high-degree and low-degree categories. The plugin also provides a shortest path detection tool for analyzing network connectivity and identifying key nodes. The plugin is implemented in Java and is based on the Cytoscape API. It includes eleven node ranking methods, including local and global methods based on shortest paths and percolated connectivity. The methods are designed to identify essential proteins and other key nodes in biological networks. CytoHubba is freely available as a Cytoscape plugin and is widely used in the scientific community for analyzing biological networks.CytoHubba is a Cytoscape plugin that identifies hub objects and sub-networks in complex interactome data. It provides 11 topological analysis methods for ranking nodes in a network, including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality, and six centralities based on shortest paths. Among these, the Maximum Neighborhood Component (MCC) method performs best in predicting essential proteins from the yeast PPI network. The plugin offers a user-friendly interface for exploring important nodes in biological networks and allows researchers to combine it with other plugins for novel analysis schemes. It computes all eleven methods in one go and provides tools for retrieving sub-networks and analyzing network topological features. CytoHubba has been downloaded over 6,700 times since 2010 and is widely used for analyzing various biological networks, including cancer metabolic networks, innate immune networks, and complex biofilm communities. CytoHubba's performance was validated by predicting essential proteins in the yeast PPI network. The results showed that MCC outperformed other methods in identifying essential proteins, particularly in both high-degree and low-degree categories. The plugin also provides a shortest path detection tool for analyzing network connectivity and identifying key nodes. The plugin is implemented in Java and is based on the Cytoscape API. It includes eleven node ranking methods, including local and global methods based on shortest paths and percolated connectivity. The methods are designed to identify essential proteins and other key nodes in biological networks. CytoHubba is freely available as a Cytoscape plugin and is widely used in the scientific community for analyzing biological networks.