The paper introduces OSLOM (Order Statistics Local Optimization Method), a novel technique for detecting clusters in networks that accounts for edge directions, weights, overlapping communities, hierarchies, and community dynamics. OSLOM is based on local optimization of a fitness function that measures the statistical significance of clusters relative to random fluctuations, using tools from Extreme and Order Statistics. The method can be used alone or as a refinement step after other techniques. It has been implemented in a freely available software and has comparable performance to the best existing algorithms on artificial benchmark graphs. The authors also present a sequential algorithm combining OSLOM with other fast techniques to handle very large networks. The method is evaluated on both artificial and real networks, demonstrating its effectiveness in various scenarios, including overlapping communities, hierarchical structures, and weighted networks. OSLOM is particularly useful for analyzing complex network datasets and can provide valuable insights into the internal organization and functional subunits of systems.The paper introduces OSLOM (Order Statistics Local Optimization Method), a novel technique for detecting clusters in networks that accounts for edge directions, weights, overlapping communities, hierarchies, and community dynamics. OSLOM is based on local optimization of a fitness function that measures the statistical significance of clusters relative to random fluctuations, using tools from Extreme and Order Statistics. The method can be used alone or as a refinement step after other techniques. It has been implemented in a freely available software and has comparable performance to the best existing algorithms on artificial benchmark graphs. The authors also present a sequential algorithm combining OSLOM with other fast techniques to handle very large networks. The method is evaluated on both artificial and real networks, demonstrating its effectiveness in various scenarios, including overlapping communities, hierarchical structures, and weighted networks. OSLOM is particularly useful for analyzing complex network datasets and can provide valuable insights into the internal organization and functional subunits of systems.