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IEEE Transactions on Network Science and Engineering

Special Issue on "Reloading Feature-rich Information Networks" 

*15 January 2020*


Sabrina Gaito, Università degli Studi di Milano, Italy
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Roberto Interdonato, CIRAD - UMR TETIS, Montpellier, France
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Tsuyoshi Murata, Tokyo Institute of Technology, Japan
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Alessandra Sala, Nokia Bell Labs, Dublin, Ireland
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Andrea Tagarelli, University of Calabria, Italy
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My T. Thai, University of Florida, USA
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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
_ 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


Manuscripts Due: 15 January 2020
Peer Reviews to Authors: 15 April 2020
1st Round Revised Manuscripts Due: 15 May 2020
2nd Round Reviews to Authors: 30 June 2020
2nd Round Revised Manuscripts Due: 30 July 2020
Final Notifications from Editors: 31 August 2020
Final Accepted Manuscripts Due: 10 September 2020


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.

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