About the Conference

Large Language Models (LLMs) have seen rapid development such as ChatGPT and LLaMA, and have received tremendous attention from both industry and academia. While they have shown remarkable success over text data, recent works have demonstrated their limitations in reasoning with structured data including graphs. Graph-structured data is ubiquitous in the real world ranging from social and biological networks to financial transactions, knowledge bases, and transportation systems – they permeate our daily lives. Therefore, understanding how to utilize graph data optimally with LLMs is a crucial research question. Recently, exploring the synergy between graphs and LLMs are attracting increasing interest in the data management and AI communities. On one hand, LLMs can be enhanced with graph computing techniques to provide answers with more contextualized facts, e.g., graph-based retrieval-augmented generation (graph RAG). On the other hand, downstream tasks, e.g., knowledge graph (KG) construction, graph data management and mining can also benefit by adopting LLMs, such as via LLM-graph DB (graph databases) and LLM-GNN (graph neural network) collaborations. It is, therefore, a timely opportunity to explore effective ways of interactions between LLMs and graph data. Our workshop “LLM+Graph” targets data management and data science researchers, aiming to inspect effective algorithms and systems to bringing LLMs, graph data management, and graph ML together in real applications.

Conference Dates