Research Topic

When Deep Learning Meets Social Networks

About this Research Topic

Deep learning is now ubiquitous, being used in many domains (computer vision, speech recognition and generation, natural language processing). Social networks are growing fast and possessing huge amounts of recorded information, which presents great opportunities in understanding the science of these big networks, and in developing new applications from and for these networks. In this collection of articles, we call for contributions that combine the two efforts, with a focus on presenting the recent advances in big network analytics using deep learning and bringing together both researchers and practitioners from different communities. Topics include, but not limited to
- network representation learning theories and foundations
- representation learning for big networks/heterogeneous networks/dynamic networks
- deep learning for networks
- graph theories and network embeddings
- visualization for network embeddings
- novel network embedding applications
- learning representations of entire networks (subnetworks)
- semi-supervised network representation learning
- network generation
- Social science theory motivated deep learning
- Adversarial leaning for social networks
- Graph convolutional network for social networks
- Explainable deep learning for social networks


Keywords: deep learning, big network analytics, dynamic networks, network embeddings, social networks


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.

Deep learning is now ubiquitous, being used in many domains (computer vision, speech recognition and generation, natural language processing). Social networks are growing fast and possessing huge amounts of recorded information, which presents great opportunities in understanding the science of these big networks, and in developing new applications from and for these networks. In this collection of articles, we call for contributions that combine the two efforts, with a focus on presenting the recent advances in big network analytics using deep learning and bringing together both researchers and practitioners from different communities. Topics include, but not limited to
- network representation learning theories and foundations
- representation learning for big networks/heterogeneous networks/dynamic networks
- deep learning for networks
- graph theories and network embeddings
- visualization for network embeddings
- novel network embedding applications
- learning representations of entire networks (subnetworks)
- semi-supervised network representation learning
- network generation
- Social science theory motivated deep learning
- Adversarial leaning for social networks
- Graph convolutional network for social networks
- Explainable deep learning for social networks


Keywords: deep learning, big network analytics, dynamic networks, network embeddings, social networks


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

21 January 2019 Manuscript
26 February 2019 Manuscript Extension

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

21 January 2019 Manuscript
26 February 2019 Manuscript Extension

Participating Journals

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

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