GraphAgent: Agentic Graph Language Assistant (2412.17029v1)

Abstract: Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this work, we propose GraphAgent, an automated agent pipeline that addresses both explicit graph dependencies and implicit graph-enhanced semantic inter-dependencies, aligning with practical data scenarios for predictive tasks (e.g., node classification) and generative tasks (e.g., text generation). GraphAgent comprises three key components: (i) a Graph Generator Agent that builds knowledge graphs to reflect complex semantic dependencies; (ii) a Task Planning Agent that interprets diverse user queries and formulates corresponding tasks through agentic self-planning; and (iii) a Task Execution Agent that efficiently executes planned tasks while automating tool matching and invocation in response to user queries. These agents collaborate seamlessly, integrating LLMs with graph LLMs to uncover intricate relational information and data semantic dependencies. Through extensive experiments on various graph-related predictive and text generative tasks on diverse datasets, we demonstrate the effectiveness of our GraphAgent across various settings. We have made our proposed GraphAgent open-source at: https://github.com/HKUDS/GraphAgent.

• The paper introduces a multi-agent framework that integrates graph reasoning with language modeling for both predictive and generative tasks.
• It employs a Graph Generator to build semantic knowledge graphs, a Task Planning Agent to structure queries, and a Task Execution Agent to bridge structured and unstructured data.
• Empirical results demonstrate over 28% improvement in zero-shot node classification and lower perplexity in text generation compared to larger language models.

Analysis of "GraphAgent: Agentic Graph Language Assistant"

The paper "GraphAgent: Agentic Graph Language Assistant" introduces an innovative multi-agent framework that integrates graph-based reasoning and LLMing to effectively handle both predictive and generative tasks involving complex data represented in structured and unstructured formats. This framework aims to address the limitations of current graph learning models, particularly their focus on explicit graph dependencies and neglect of implicit graph-enhanced semantic dependencies found in real-world data scenarios.

Core Components of GraphAgent

GraphAgent incorporates three fundamental agents:

1. Graph Generator Agent: This component is responsible for constructing Semantic Knowledge Graphs (SKGs) from textual inputs. Unlike conventional graph learning approaches that prioritize explicit relational information, the Graph Generator captures rich contextual and implicit entity-wise dependencies. It employs an iterative two-phase workflow that initially extracts scaffold nodes or high-level entities, followed by knowledge augmentation to refine node descriptions with detailed semantic insights.
2. Task Planning Agent: This agent interprets user queries and transforms them into structured tasks, leveraging both the explicit graph data and the semantic graphs generated by the Graph Generator. It efficiently plans predictive and generative tasks by converting graph data into tokenized representations suitable for LLMs.
3. Task Execution Agent: Also known as the Graph Action Agent, it uses advanced graph language architectures to execute planned tasks, effectively bridging the gap between structured graph comprehension and unstructured language understanding. This agent empowers the GraphAgent to excel in a wide range of graph-related predictive tasks and open-ended text generation, by leveraging the enhanced embeddings produced by the prior agents.

Numerical Evaluation and Results

Through extensive experiments, the efficacy of GraphAgent is validated across various datasets and tasks, including both predictive graph-related tasks and generative text production scenarios. Remarkably, the framework demonstrates strong performance by integrating structured and unstructured data, showing robust results even with relatively smaller models compared to state-of-the-art LLMs. Specifically, GraphAgent achieves an average improvement of over 28% in zero-shot node classification tasks compared to top-performing graph LLMs, evidencing its robust semantic integration capability.

Moreover, in text generation tasks, GraphAgent achieves lower perplexity scores compared to larger LLMs, showcasing its fluency and coherence in content production. The framework's ability to maintain high generative performance, despite smaller parameter sizes, underscores the efficacy of its graph-enhanced tokenization strategy and alignment training in capturing complex reasoning patterns inherent in the tasks.

Implications and Future Directions

The convergence of graph learning and LLMing within the GraphAgent framework presents significant implications for both practical applications and theoretical advancements. Practically, GraphAgent offers a user-friendly analytical tool capable of processing varied data types with minimal requirement for specialized knowledge. Theoretically, it provides insights into enhancing model generalization by leveraging structured semantic representations alongside LLMs.

Looking forward, this research may catalyze further exploration into even more integrated model architectures, potentially incorporating multi-modal data, such as visual inputs, to broaden the framework's applicability. Additionally, future investigations might focus on refining the agent coordination strategies to further optimize the balance between predictive precision and generative versatility.

In conclusion, "GraphAgent: Agentic Graph Language Assistant" marks a substantial advancement in the integration of graph-based reasoning with LLMing. Its innovative approach to handling both explicit and implicit data dependencies presents new possibilities for AI systems capable of more complex analytical tasks, bridging the gap between structured and unstructured data processing in contemporary artificial intelligence applications.