English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Graph plays a significant role in representing and analyzing complexrelationships in real-world applications such as citation networks, socialnetworks, and biological data. Recently, Large Language Models (LLMs), whichhave achieved tremendous success in various domains, have also been leveragedin graph-related tasks to surpass traditional Graph Neural Networks (GNNs)based methods and yield state-of-the-art performance. In this survey, we firstpresent a comprehensive review and analysis of existing methods that integrateLLMs with graphs. First of all, we propose a new taxonomy, which organizesexisting methods into three categories based on the role (i.e., enhancer,predictor, and alignment component) played by LLMs in graph-related tasks. Thenwe systematically survey the representative methods along the three categoriesof the taxonomy. Finally, we discuss the remaining limitations of existingstudies and highlight promising avenues for future research. The relevantpapers are summarized and will be consistently updated at:https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.

A Survey of Graph Meets Large Language Model: Progress and Future Directions