title: Community analysis in directed networks: in-, out-, and pseudocommunities.
abstract: When analyzing important classes of complex interconnected systems, link directionality can hardly be neglected if a precise and effective picture of the structure and function of the system is needed. If community analysis is performed, the notion of "community" itself is called into question, since the property of having a comparatively looser external connectivity could refer to the inbound or outbound links only or to both categories. In this paper, we introduce the notions of in-, out-, and in-/out-community in order to correctly classify the directedness of the interaction of a subnetwork with the rest of the system. Furthermore, we extend the scope of community analysis by introducing the notions of in-, out-, and in-/out-pseudocommunity. They are subnetworks having strong internal connectivity but also important interactions with the rest of the system, the latter taking place by means of a minority of its nodes only. The various types of (pseudo-)communities are qualified and distinguished by a suitable set of indicators and, on a given network, they can be discovered by using a "local" searching algorithm. The application to a broad set of benchmark networks and real-world examples proves that the proposed approach is able to effectively disclose the different types of structures above defined and to usefully classify the directionality of their interactions with the rest of the system.
authors.affiliation: Department of Electronics, Information and Bioengineering, Politecnico di Milano, I-20133 Milano, Italy.
authors.forename: Pietro
authors.initials: P
authors.lastname: Landi
authors.affiliation: Department of Electronics, Information and Bioengineering, Politecnico di Milano, I-20133 Milano, Italy.
authors.forename: Carlo
authors.initials: C
authors.lastname: Piccardi
mesh_terms: D003198:Computer Simulation; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical