Call for Workshop Papers
Online Social Networks (OSNs) are becoming the most important medium for information interchange among people. The wide availability and huge growth of social network data have made it tough or even intractable to be analyzed with traditional tools and technologies. Meanwhile, deep learning technology powers many aspects of social network analysis, such as online community detection, reputation systems, question answering systems, prediction systems, recommendation systems, and heterogeneous information network analysis. In structure analysis aspect, deep learning allows computational models that are composed of multiple processing layers to learn representations of social network data with multiple levels of abstraction. For social content analysis, deep learning has brought about breakthroughs in processing natural language, images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
All registered workshop papers will be submitted together with EAI ICMTEL 2021 proceedings for publishing by Springer to be made available through SpringerLink Digital Library.
- Deep learning assisted user behavior analysis
- Deep representation learning for online social network
- Deep learning model assisted sentiment analysis for social network platform
- Deep learning for community discovery in online social networks
- Deep learning driven social multimedia sentiment analysis
- Deep learning assisted urban information system for online social network
- Deep learning assisted wireless power transfer in online social networks
- Deep learning-based schedule and optimization of social robotic network
- Deep learning in data collection on social network
- Deep learning for online social service computing
- Deep learning for social media analysis in health management
- Advanced deep learning algorithms and models
Prof. Pengjiang Qian, Jiangnan University, China