Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …

The long-tail recommendation is a challenging task for traditional recommender systems,
due to data sparsity and data imbalance issues. The recent development of large language …

Large language models (LLMs) have achieved impressive success across various domains,
but their capability in understanding and resolving complex graph problems is less explored …

The" arms race" of Large Language Models (LLMs) demands novel, challenging, and
diverse benchmarks to faithfully examine their progresses. We introduce GraphArena, a …

Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks,
there's growing interest in applying LLMs to graph-related tasks. This study delves into the …

Given the ubiquity of graph data and its applications in diverse domains, building a Graph
Foundation Model (GFM) that can work well across different graphs and tasks with a unified …

Large language models (LLMs) demonstrate great potential for problems with implicit
graphical structures, while recent works seek to enhance the graph reasoning capabilities of …