posted by organizer: tagarelli || 1818 views || tracked by 1 users: [display]

ReloadNets 2020 : CfP - Special Issue on Reloading Feature-rich Information Networks, IEEE TNSE

FacebookTwitterLinkedInGoogle

 
When N/A
Where N/A
Submission Deadline TBD
Categories    network science   machine learning   deep learning   big data
 

Call For Papers

CALL FOR PAPERS
IEEE Transactions on Network Science and Engineering

Special Issue on "Reloading Feature-rich Information Networks"


GUEST EDITORS:

Sabrina Gaito, Università degli Studi di Milano, Italy
(gaito@di.unimi.it)

Roberto Interdonato, CIRAD - UMR TETIS, Montpellier, France
(roberto.interdonato@cirad.fr)

Tsuyoshi Murata, Tokyo Institute of Technology, Japan
(murata@c.titech.ac.jp)

Alessandra Sala, Nokia Bell Labs, Dublin, Ireland
(alessandra.sala@nokia-bell-labs.com)

Andrea Tagarelli, University of Calabria, Italy
(andrea.tagarelli@unical.it)

My T. Thai, University of Florida, USA
(mythai@cise.u.edu)



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: December 2, 2019
• Peer reviews to authors: February 15, 2020
• 1st round revised manuscripts due: March 15, 2020
• 2nd round reviews to authors: April 30, 2020
• 2nd round revised manuscripts due: May 30, 2020
• Final notifications from editors: June 30, 2020
• Final accepted manuscripts due: July 10, 2020


SUBMISSION GUIDELINES:

Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Network Science and Engineering guidelines (https://www.comsoc.org/publications/journals/ieee-tnse/ieee-transactions-network-science-and-engineering-information).
Note that the page limit is the same as that of regular papers. Please submit your papers through the online system (https://mc.manuscriptcentral.com/tnse-cs) 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.

Related Resources

ECML PKDD 2020   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ICDM 2021   21th Industrial Conference on Data Mining
ICENT--EI Compendex, Scopus 2021   2021 3rd International Conference on Emerging Networks Technologies (ICENT 2021)--Ei Compendex, Scopus
PAKDD 2021   Pacific-Asia Conference on Knowledge Discovery and Data Mining
ICENT--EI, Scopus 2021   2021 3rd International Conference on Emerging Networks Technologies (ICENT 2021)--Ei Compendex, Scopus
MLDM 2021   17th International Conference on Machine Learning and Data Mining
ITAS--EI Compendex, Scopus 2021   2021 Information Technology & Applications Symposium (ITAS 2021)--EI Compendex, Scopus
IARCE 2021-Ei Compendex & Scopus 2021   2021 5th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2021)
AS-RLPMTM 2021   Applied Sciences special issue Rich Linguistic Processing for Multilingual Text Mining
CFDSP 2021   2021 International Conference on Frontiers of Digital Signal Processing (CFDSP 2021)