Call for Papers
Special Issue on Neural Information and Knowledge Processing for Social Network Analysis
Social networks have become the primary means of online communication, gaining significant attention across various sectors including education, medicine, and business. The increasing communication on these platforms provides valuable insights into human behavior, such as social interactions, opinion mining, and preferences. However, mining exploitable data from social networks within available low-dimensional space remains challenging.
The advent of deep representation learning, combining knowledge processing with deep neural networks, has remarkably enhanced social network analysis. This approach transforms network elements into knowledge vectors, reducing redundancy without compromising quality. Integration with various neural information processing networks efficiently decodes detailed information, handling the dynamic nature of nodes similar to real-life conditions.
Deep representation learning preserves hierarchical network structures, enhancing sentiment and opinion analysis precision. It also improves natural language processing accuracy and can be extended to other fields of social media analytics. Despite these advantages, the field faces several challenges, including constructing knowledge vectors without information loss, quality control for global adoption, developing common methodologies for representing human emotions, and unified representation of multimodal data.
This special issue aims to discuss and highlight various aspects of emerging trends in deep representative learning and related fields for social network analysis. We invite researchers to present their work on Neural Information and Knowledge Processing for Social Network Analysis.
Topics of interest include, but are not limited to:
- Advances in text mining representations using language model objectives
- Evaluation of existing representations through generalized analytics
- Unsupervised, semi-supervised, and supervised representation learning for social media analytics
- Sparse representation and coding for image processing
- Manifold learning, subspace learning, and dimensionality reduction for sentiment analysis
- Integration of hierarchical networks in social network analysis
- Probabilistic graphical models for opinion and emotion representation
- Positive-unlabeled representation learning for psychological analysis
- Multi-instance and complementary representation learning in natural language processing
- Label-noise and partial-/superset-label representation learning for social media data
- Theoretical frameworks for learning with noisy data in an open world
- Deep representative understanding for model adaptation in changing online environments
- Applications of deep representative learning in open-world sensing
Submission:
Submissions are to be submitted and reviewed on the Editorial Manager system: https://www2.cloud.editorialmanager.com/ijcia/default2.aspx
When submitting your paper, first, in the "Article Type" field, select "Special Issue". Then later in the "Section/Category" field, select "Special Issue: Neural Information and Knowledge Processing for Social Network Analysis".
For the formatting of your paper, please download and use this style layout MS Word template: https://www.worldscientific.com/sda/1037/ijcia-doc.zip