Summary of Graphteam: Facilitating Large Language Model-based Graph Analysis Via Multi-agent Collaboration, by Xin Sky Li et al.
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
by Xin Sky Li, Qizhi Chu, Yubin Chen, Yang Liu, Yaoqi Liu, Zekai Yu, Weize Chen, Chen Qian, Chuan Shi, Cheng Yang
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed GraphTeam system, based on Large Language Models (LLMs), is a multi-agent framework for graph analysis that combines the strengths of different LLM-based agents to tackle complex problems. The system consists of five agents: question, answer, search, coding, and reasoning, each with its own specialty. The question agent refines key arguments from the original question, while the answer agent organizes results to meet output requirements. The search agent retrieves relevant information from a built knowledge base, which is then used by the coding agent to generate solutions using established algorithms. If the coding agent fails, the reasoning agent directly computes results without programming. GraphTeam achieves state-of-the-art performance on six graph analysis benchmarks with an average 25.85% improvement over the best baseline in terms of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphTeam is a new way for computers to analyze graphs, like social networks or maps. Right now, there are two main ways to do this: using special computer programs called Graph Neural Networks (GNNs) or relying on Large Language Models (LLMs). But these methods have some limitations. GNNs can only be used for specific tasks and aren’t very good at adapting to new situations. LLMs don’t work well because they don’t have any external knowledge or tools to help them solve problems. To fix this, scientists created GraphTeam, a system that uses multiple LLM-based agents working together to analyze graphs. Each agent has its own special skill, like asking questions or finding relevant information. They can share their skills with each other to get the best results. This helps GraphTeam solve complex problems much better than existing methods. |
Keywords
» Artificial intelligence » Knowledge base
