Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

Artificial intelligence (AI) has significantly impacted various fields. Large language models
(LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have …

Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

Heterogeneous graph learning aims to capture complex relationships and diverse relational
semantics among entities in a heterogeneous graph to obtain meaningful representations …

While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-
Euclidean structural data, they are difficult to be deployed in real applications due to the …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Machine Unlearning (MU) algorithms have become increasingly critical due to the
imperative adherence to data privacy regulations. The primary objective of MU is to erase …

Graph Neural Networks (GNNs) have demonstrated a great representation learning
capability on graph data and have been utilized in various downstream applications …