GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning

2026-01-13

AI Summary

Existing search-augmented large reasoning models struggle to effectively leverage the topological information of graph structures, leading to inefficient retrieval and reasoning in zero-shot graph learning tasks. This work proposes GraphSearch, a novel framework that extends search-augmented reasoning to graph learning for the first time. GraphSearch employs a graph-aware query planner and a graph-aware retriever that jointly decouple semantic queries from the graph search space, introducing a hybrid retrieval mechanism that integrates both topological and semantic information. The framework supports both recursive and flexible graph traversal strategies, enabling efficient zero-shot inference without task-specific fine-tuning. Evaluated across multiple benchmark datasets, GraphSearch achieves state-of-the-art performance in zero-shot node classification and link prediction, matching or surpassing supervised methods.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability to operate on graph-structured data, prevalent in domains such as e-commerce, social networks, and scientific citations, remains underexplored. Unlike plain text corpora, graphs encode rich topological signals that connect related entities and can serve as valuable priors for retrieval, enabling more targeted search and improved reasoning efficiency. Yet, effectively leveraging such structure poses unique challenges, including the difficulty of generating graph-expressive queries and ensuring reliable retrieval that balances structural and semantic relevance. To address this gap, we introduce GraphSearch, the first framework that extends search-augmented reasoning to graph learning, enabling zero-shot graph learning without task-specific fine-tuning. GraphSearch combines a Graph-aware Query Planner, which disentangles search space (e.g., 1-hop, multi-hop, or global neighbors) from semantic queries, with a Graph-aware Retriever, which constructs candidate sets based on topology and ranks them using a hybrid scoring function. We further instantiate two traversal modes: GraphSearch-R, which recursively expands neighborhoods hop by hop, and GraphSearch-F, which flexibly retrieves across local and global neighborhoods without hop constraints. Extensive experiments across diverse benchmarks show that GraphSearch achieves competitive or even superior performance compared to supervised graph learning methods, setting state-of-the-art results in zero-shot node classification and link prediction. These findings position GraphSearch as a flexible and generalizable paradigm for agentic reasoning over graphs.

Problem

• graph learning
• zero-shot learning
• search-augmented reasoning
• topological signals
• graph-structured data

Innovation

• search-augmented reasoning
• zero-shot graph learning
• graph-aware retrieval
• topological signals
• agentic reasoning