Feature-rich networks: going beyond complex network topologies

• Published in: Applied network science Vol. 4; no. 1; pp. 1 - 13
• Main Authors: Interdonato, Roberto, Atzmueller, Martin, Gaito, Sabrina, Kanawati, Rushed, Largeron, Christine, Sala, Alessandra
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 31.01.2019; Springer; SpringerOpen
• Subjects: Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Machine Learning; Review; Simulation and Modeling
• ISSN: 2364-8228; 2364-8228
• DOI: 10.1007/s41109-019-0111-x

The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Feature-rich Networks , i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features . The aim is also to sketch a scenario that can inspire the design of novel feature-rich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domain-specific applications.