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 task of multi-hop link prediction within knowledge graphs (KGs) stands as a challenge in
the field of knowledge graph analysis, as it requires the model to reason through and …

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

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

Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet
leveraging large language models (LLMs) for TAGs presents unique challenges due to the …

Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them
suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm …

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 …

Offline evaluation of LLMs is crucial in understanding their capacities, though current
methods remain underexplored in existing research. In this work, we focus on the offline …