GraphAgent - An intelligent graphical language assistant open-sourced by the University of Hong Kong and the Hong Kong University of Science and Technology.
GraphAgent is an intelligent graphical language assistant jointly developed by the University of Hong Kong and the Hong Kong University of Science and Technology (Guangzhou). It can process both structured (like graph connections) and unstructured (like text and visual information) data from the real world...
What is GraphAgent?
GraphAgent is an intelligent graphical language assistant jointly developed by the University of Hong Kong and the Hong Kong University of Science and Technology (Guangzhou). It can process both structured (like graph connections) and unstructured (like text and visual information) data from the real world, containing complex relationships and dependencies, which can be visualized using knowledge graphs. GraphAgent comprises three key components: a graph generation agent that builds the knowledge graph, a task planning agent that interprets user queries and formulates tasks, and a task execution agent that executes tasks and automates tool matching. GraphAgent integrates language models and graph language models to reveal complex relationships and semantic dependencies between data, demonstrating effectiveness on various graph-related tasks.
Main functions of GraphAgent
- Graph generationAutomatically construct knowledge graphs that reflect complex semantic dependencies and extract entity nodes and relationships from text.
- Task planningInterpret user queries, transform user needs into specific prediction or generation tasks, and plan execution strategies.
- Task executionExecute planned tasks, including matching and calling automated tools, and responding to user queries.
- Natural Language InteractionIt supports users to interact with the system using natural language, allowing them to analyze graph data without requiring specialized knowledge.
- Predictive AnalysisIt supports graph-based prediction tasks, such as node classification and link prediction.
Technical principles of GraphAgent
- Heterogeneous diagram representationHeterogeneous graphs are used to represent structured and unstructured data and capture the relationships between entities.
- Graph Neural NetworksGraph neural networks, acting as graph tokenizers, encode graph structure information into embedded representations.
- Large language modelIt combines pre-trained large-scale language models to understand and generate natural language, along with graph structure information.
- Proxy architectureDesign a multi-agent system, where each agent is responsible for handling different tasks, such as generation, planning, and execution.
- Image-Instruction AlignmentTraining large-scale language models based on graph-instruction matching tasks enables better understanding and processing of graph-structured data.
GraphAgent project address
- GitHub repository:https://github.com/HKUDS/GraphAgent
- HuggingFace model library:https://huggingface.co/GraphAgent
- arXiv technical paper:https://arxiv.org/pdf/2412.17029
Application scenarios of GraphAgent
- Academic Network AnalysisBased on the construction of citation relationship maps of academic literature, it helps researchers discover important papers and research trends in the field.
- E-commerce recommendationsIn e-commerce platforms, analyzing user purchasing and browsing behavior provides personalized product recommendations.
- Financial risk managementIt is used to build financial transaction networks, identify potential risks and fraudulent activities, and assist in decision-making.
- Social network analysisAnalyze connections and interactions in social networks to reveal community structure and influential nodes.
- Content recommendation systemBased on users' historical behavior and preferences, we recommend news articles or video content that they may be interested in.