Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Foundation models have emerged as critical components in a variety of artificial intelligence
applications, and showcase significant success in natural language processing and several …

In the realm of personalized recommender systems, the challenge of adapting to evolving
user preferences and the continuous influx of new users and items is paramount …

Researching genes and their interactions is crucial for deciphering the fundamental laws of
cellular activity, advancing disease treatment, drug discovery, and more. Large language …

Inspired by the success of foundation models in applications such as ChatGPT, as graph
data has been ubiquitous, one can envision the far-reaching impacts that can be brought by …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Constructing accurate gene regulatory network s (GRNs), which reflect the dynamic
governing process between genes, is critical to understanding the diverse cellular process …

Molecular property prediction stands as a cornerstone task in AI-driven drug design and
discovery, wherein the atoms within a molecule serve as nodes, collectively forming a graph …

Zero-Shot Hashing (ZSH) has aroused significant attention due to its efficiency and
generalizability in multi-modal retrieval scenarios, which aims to encode semantic …

The pre-train then fine-tune approach has advanced GNNs by enabling general knowledge
capture without task-specific labels. However, an objective gap between pre-training and …