Research Topic

Methods and applications for large-scale structured relational data

About this Research Topic

In many application fields, from healthcare to education and social sciences, it is nowadays very easy to collect large amounts of unstructured, possibly unlabelled data, but it is still an open challenge how to deal with it in terms of knowledge extraction and relational structure inference. To tackle this problem, in the broad field of artificial intelligence, many approaches have been proposed ranging from deep learning to probabilistic models based on suitable graph representations. Among other applications that already have an impact in daily life, we mention:

• the use of network representations to make sense of large corpora of multi-language textual data
• to infer the evolution of complex systems evolving over time such as predicting new links forming on a social network, understanding motion dynamics in gait analysis, devising the behavior of cellular networks.

This research focuses on modeling the relational nature of the available data while incorporating structural and topological constraints on the inferred network. In particular, we are interested in devising novel statistical approaches for the inference of generative models able to describe the complex relations underlying data with a given topological structure, that may be provided a priori.

In this Research Topic, we invite contributions that introduce new fundamental methods or demonstrate novel applications to a particular domain when dealing with structured data that show an intrinsic relational nature. Potential topics include, but are not restricted to:

• Structured data/Structured machine learning
• Unsupervised learning
• Network inference
• NLP and text mining
• Network analytics
• Knowledge discovery
• Web search and Web mining
• Probabilistic learning and topic modeling
• Deep learning/Embeddings
• Kernel methods

We accept manuscripts of the following types: Original Research, Systematic Review, Methods, Review, Mini Review, Perspective, Data Report, Brief Research Report, Technology, and Code.


Keywords: Block modelling, Topic modelling, Unsupervised learning, Graphical models, Network inference, NLP, Text mining, Network analysis, Statistical testing, Knowledge discovery, Graph mining, Web search, Web mining, Probabilistic learning, Bayesan learning


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.

In many application fields, from healthcare to education and social sciences, it is nowadays very easy to collect large amounts of unstructured, possibly unlabelled data, but it is still an open challenge how to deal with it in terms of knowledge extraction and relational structure inference. To tackle this problem, in the broad field of artificial intelligence, many approaches have been proposed ranging from deep learning to probabilistic models based on suitable graph representations. Among other applications that already have an impact in daily life, we mention:

• the use of network representations to make sense of large corpora of multi-language textual data
• to infer the evolution of complex systems evolving over time such as predicting new links forming on a social network, understanding motion dynamics in gait analysis, devising the behavior of cellular networks.

This research focuses on modeling the relational nature of the available data while incorporating structural and topological constraints on the inferred network. In particular, we are interested in devising novel statistical approaches for the inference of generative models able to describe the complex relations underlying data with a given topological structure, that may be provided a priori.

In this Research Topic, we invite contributions that introduce new fundamental methods or demonstrate novel applications to a particular domain when dealing with structured data that show an intrinsic relational nature. Potential topics include, but are not restricted to:

• Structured data/Structured machine learning
• Unsupervised learning
• Network inference
• NLP and text mining
• Network analytics
• Knowledge discovery
• Web search and Web mining
• Probabilistic learning and topic modeling
• Deep learning/Embeddings
• Kernel methods

We accept manuscripts of the following types: Original Research, Systematic Review, Methods, Review, Mini Review, Perspective, Data Report, Brief Research Report, Technology, and Code.


Keywords: Block modelling, Topic modelling, Unsupervised learning, Graphical models, Network inference, NLP, Text mining, Network analysis, Statistical testing, Knowledge discovery, Graph mining, Web search, Web mining, Probabilistic learning, Bayesan learning


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.

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Submission Deadlines

15 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

15 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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