Topic Summary

The growing availability of multi-faceted relational data gives rise to unprecedented opportunities for unveiling complex real-world behaviors and phenomena. This also supports the proliferation of complex network models where the expressive power of the graph-based relational structure is enhanced through exposing several types of features that are peculiar of the domain-specific environment (e.g., social media platforms, biological environment, geographical location, etc.). Examples of this kind of feature-rich networks include Heterogeneous information networks, Multilayer networks, Temporal networks, Location-aware networks, and Probabilistic networks.

The aim of this Special Issue, titled Reloading Feature-rich Information Networks, is to address challenging issues and emerging trends in feature-rich information networks that can be mined in several domains, including not only long studied contexts such as social media and biology, but also less investigated or even new frontiers for network science, such as finance, engineering, archaeology, geology, astronomy, and many others. Although the use of feature-rich networks can intuitively be perceived as beneficial for most research tasks based on graph data, their expressive power has not been yet fully valued in most domains, therefore there is an emergence for providing insights into how the study of complex network models can pave the way for solving domain-specific problems that might not be adequately addressed by existing graph models.

Within this view, we solicit contributions on advanced modeling and mining of feature-rich networks, regarding any data domain, including both theoretical and application-oriented studies. In particular, we encourage contributions on the development of novel approaches based on advanced optimization techniques and learning paradigms (e.g., online learning, reinforcement learning, and deep learning) to enhance our understanding of complex phenomena in information networks, but also visionary works about alternative modeling and mining approaches for complex networks.

The topics of interest for this special issue include, but are not limited to:

• Foundations of Learning and Mining in feature-rich networks
• Simplification/pruning/sampling of feature-rich networks
• Embedding and Deep Learning in feature-rich networks
• Centrality and Ranking in feature-rich networks
• Vertex similarity in multiplex and feature-rich networks
• Community Detection in feature-rich networks
• Link Prediction in feature-rich networks
• Multiplex and feature-rich networks evolution models
• Ensemble learning for feature-rich networks mining
• Pattern mining in feature-rich networks
• User Behavior Modeling in feature-rich networks
• Influence propagation in feature-rich networks
• Reputation and Trust computing in feature-rich networks
• Probabilistic and Uncertain feature-rich networks
• Time-evolving feature-rich networks
• Hypergraph-based modeling, analysis and learning problems
• Cross-Domain problems in feature-rich networks
• Mobility in feature-rich networks
• Visualization of feature-rich networks

Important Dates

• Manuscripts Due: 2 December 2019
• Peer Reviews to Authors: 15 February 2020
• 1st Round Revised Manuscripts Due: 15 March 2020
• 2nd Round Reviews to Authors: 30 April 2020
• 2nd Round Revised Manuscripts Due: 30 May 2020
• Final Notifications from Editors: 30 June 2020
• Final Accepted Manuscripts Due: 10 July 2020

Submission Guidelines

Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Network Science and Engineering guidelines.

Note that the page limit is the same as that of regular papers. Please submit your papers through the online system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail to the Guest Editors directly.

Guest Editors