Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Abstract Machine learning, particularly graph learning, is gaining increasing recognition for
its transformative impact across various fields. One such promising application is in the …

While large language models (LLMs) have integrated images, adapting them to graphs
remains challenging, limiting their applications in materials and drug design. This difficulty …

Graph is a prevalent discrete data structure, whose generation has wide applications such
as drug discovery and circuit design. Diffusion generative models, as an emerging research …

Metamaterials, synthetic materials with customized properties, have emerged as a promising
field due to advancements in additive manufacturing. These materials derive unique …

Biologists frequently desire protein inhibitors for a variety of reasons, including use as
research tools for understanding biological processes and application to societal problems …

Generating novel molecules is challenging, with most representations leading to generative
models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) …

Polymeric membranes have become essential for energy-efficient gas separations such as
natural gas sweetening, hydrogen separation, and carbon dioxide capture. Polymeric …

The data on user behaviors is sparse given the vast array of useritem combinations.
Attributes related to users (eg, age), items (eg, brand), and behaviors (eg, co-purchase) …