Artificial Benchmark for Community Detection with Outliers (ABCD+o)

One of the most important features of real-world networks is their community structure, as it reveals the internal organization of nodes [10]. In social networks, communities may represent groups by interest; in citation networks, they correspond to related papers; on the Web, communities are formed by pages on related topics, etc. Being able to identify communities in a network could help us to exploit this network more effectively. Grouping like-minded users or similar-looking items together is important for a wide range of applications, including controlling epidemics [12], recommendation systems, anomaly or outlier detection, fraud detection, rumor or fake news detection, etc. [16]. There is also growing literature introducing community-aware centrality measures that exploit both local and global properties of networks [8, 35]. For more discussion around various aspects of mining complex networks, see for example, [31, 23].