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
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
