About this Research Topic
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.
This Research Topic also welcomes contributions presented at the following two events in the form of extended papers:
• MARAMI 2020 - Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques, The 11th French Conference on Network Modeling and Analysis, organized by the UMR TETIS (Montpellier, France) and to be held a virtual event on October 14 - 15, 2020
• Soc2Net 2020 – 2nd International Workshop on Modeling and Mining Social-Media-Driven Complex Networks, to be held as a virtual event on December 7, 2020, in conjunction with The international conference series on Advances in Social Network Analysis and Mining (ASONAM 2020). The workshop is organized by Roberto Interdonato (Cirad, UMR TETIS, Montpellier, France), Sabrina Gaito (Università di Milano, Italy), Andrea Tagarelli (Università della Calabria, Italy) and Alessandra Sala (Nokia Bell Labs, Dublin, Ireland).
Topics that may be included are:
• 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.