Context

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 base. 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 the MATNet workshop, that will be held in conjunction with The Web Conf 2018 in Lyon, 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 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, possibly being  transversal to different application domains. In particular, we cover the modeling of complex networks, multiplex networks, and any unsupervised, supervised, and semi-supervised mining approach in attributed network contexts.

Topics 

– A list of non-exhaustive relevant topics include:
– Models and measures for multiplex  & attributed networks.
– Foundations of Learning and Mining in Attributed Networks
– Centrality and Ranking in Attributed Networks
– Community Detection in Attributed Networks
– Link Prediction in Attributed Networks
– Simplification/pruning/sampling of Attributed Networks
– User Behavior Modeling in Attributed Networks
– Social Influence and Information diffusion in Attributed Networks
– Reputation and Trust in Attributed Networks
– Embedding and Deep Learning in Attributed Networks
– Probabilistic and Uncertain Attributed Networks
– Time-evolving Attributed Networks
– Hypergraph-based modeling, analysis and learning problems
– Cross-Domain problems in Attributed Networks
– Visualization of Attributed Networks
– Personalization and Recommendation in Attributed Networks
– Mobility in Attributed Networks
– Vertex similarity in multiplex and attributed networks
– Multiplex and attributed networks evolution models
– Multiplex network and dynamic network mining
– Ensemble learning for multiplex and attributed network mining
– Pattern mining in attributed networks