The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Neural architecture search (NAS) has been proposed to automatically tune deep neural
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …

Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it
has gradually become the leading approach in many fields. It is currently playing a vital role …

Abstract Social Network Analysis (SNA) has been a popular field of research since the early
1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering …

Recent methods in sequential recommendation focus on learning an overall embedding
vector from a user's behavior sequence for the next-item recommendation. However, from …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …