Connectionists: CfP: "Digging into Feature-Rich Networks: unveiling connections in Big Data", Special Issue of Frontiers in Big Data

[Roberto Interdonato]

CALL FOR PAPERS
Frontiers in Big Data


Special Issue on "Digging into Feature-Rich Networks: unveiling connections
in Big Data"

https://www.frontiersin.org/research-topics/16784/digging-into-feature-rich-networks-unveiling-connections-in-big-data



GUEST EDITORS:

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

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

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



TOPIC SUMMARY:

The recent progress of methodologies and technologies in the fields of Big
Data Analytics, from one side, and Machine/Deep Learning, from another
side, has given a new impulse to research on complex real-world behaviors
and phenomena, which can be conveniently modeled as feature-rich networks.
Such networks are designed to take the expressive power of graph data
models of different types (e.g., Heterogeneous information networks,
Multilayer networks, Temporal networks, Location-aware networks,
Probabilistic networks) to a higher level whereby the contextual
information of a target domain-specific environment are embedded into the
network.

The aim of this Research Topic, titled “Digging into Feature-Rich Networks:
unveiling connections in Big Data”, is to address challenging issues and
emerging trends at the convergence of Network Science with Big Data
Analytics and Machine/Deep Learning. The idea is to encourage the
development of novel approaches based on advanced big data techniques and
learning paradigms, that will enhance our understanding of complex
phenomena in information networks. Moreover, we also aim to promote
visionary works about alternative approaches for complex network analysis.

These include not only long studied contexts such as social media and
biological networks, 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.

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
• Modeling and Analysis of IoT-based feature-rich networks
• Smart environment and smart city management with feature-rich networks


Keywords: Network Science, Big Data Analytics, Deep Learning, Machine
Learning, Information Networks, Feature-Rich Networks, Networks Mining

Important Note: All contributions to this Research Topic must be within the
scope of the section and journal to which they are submitted, as defined in
their mission statements. Frontiers reserves the right to guide an
out-of-scope manuscript to a more suitable section or journal at any stage
of peer review.



IMPORTANT DATES:

Abstract : 31 January 2021
Manuscripts Due: 30 April 2021


SUBMISSION GUIDELINES:

Manuscript and abstracts can be submitted through the dedicated website at
:
https://www.frontiersin.org/research-topics/16784/digging-into-feature-rich-networks-unveiling-connections-in-big-data
.
The authors can choose between two different sections of Frontiers in Big
Data at the moment of submission (Big Data Networks or Data Mining and
Management). This does not impact on the peer reviewing process, but only
on the categorization of the article in case of acceptance.
-- 

=============================================
Roberto Interdonato, PhD
Research Scientist
CIRAD
UMR TETIS
Montpellier
France
email: roberto.interdonato at cirad.fr
tel: +33 (0) 467558615
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