Attributed network models have seen an increasing success in recent years, thanks to their informative power and to their ability to model complex networked relations that characterize most real-world phenomena. Their use has been attractive to communities in different disciplines such as computer science, physics, social science, as well as in interdisciplinary research environments. The use of such models has been also supported by the increasing easiness in collecting multi-relational data from the Web, e.g., from online social media platforms, crowdsourced data, online knowledge bases; within this view, the World Wide Web is an inestimable source of information, which can be conveniently represented with feature-rich network models, e.g., enclosing temporal aspects of the data, quantitative and/or qualitative properties of nodes, different relations between a common set of entities, different existence probabilities, or modeling connection between different entity types.

The aim of this workshop,  is to get an insight in the current status of research in network analysis and mining, showing how modeling information coming from the World Wide Web in Attributed Network models can make it possible to focus on domains and research questions that have not been deeply investigated so far and to improve solutions to classic tasks. We will encourage contributions on methods and techniques that are transversal to different application domains, rather than focusing on specific issues concerning each domain separately. We will consider the two main aspects of network analysis: modeling and knowledge discovery. The technical sessions should point out this differentiation, and enforce the interaction between researchers from different domains.

We solicit contributions that aim to focus on the analysis of attributed networks, addressing important principles, methods, tools and future research directions in this emerging field. In particular, we cover the modeling of complex networks, multiplex networks, and any unsupervised, supervised, and semi-supervised mining approach in attributed network contexts.