Retrieval-Augmented Generation (RAG) was proposed to address the hallucination question of large language models (LLMs). However, the traditional RAG framework has certain limitations: for simple questions, the search results often introduce a large amount of irrelevant information; while for complex questions, the lengthy reference knowledge provided by the retrieval lacks structural information. Therefore, we proposed a structure-aware RAG, which achieves noise removal in retrieval through multi-chain graph navigation reasoning(Trig-Nav). This method constructs question triple reasoning chains and reference knowledge graphs with text attributes, allowing the system to retrieve three types of knowledge along different paths based on the requirements of LLM. It provides LLM with multi-angle and structured information input and significantly reduces noise. We conducted a comprehensive evaluation of Trig-Nav, comparing it with baseline methods across multiple datasets.Compared to traditional RAG, there is an average improvement of 6% in effectiveness. The results showed that Trig-Nav significantly enhances the model’s performance, validating the effectiveness of this approach.

Graph learning requires jointly leveraging node semantics and graph structure, but traditional methods often suffer from label scarcity and shallow semantic utilization. Although LLMs provide strong semantic reasoning for graph learning, existing LLM-augmented pipelines face two major challenges: their semantics-only outputs can inject semantic noise that conflicts with graph topology, and they often incur substantial overhead due to LLM fine-tuning or repeated LLM calls at training/inference. We propose CoFGraph, a confidence-feedback framework that interleaves reciprocal calibration between semantic and structural views to enable reliable and efficient LLM-augmented graph learning. To avoid expensive LLM adaptation, we keep the LLM frozen and train a lightweight Interpreter as a semantic proxy, eliminating the need for LLM fine-tuning and continual test-time prompting. To further mitigate semantic noise and the lack of structural feedback, we introduce confidence-based dual-view learning with node-wise confidence estimation and dynamic role switching, allowing the more reliable view to correct the other. Experiments on four public benchmarks validate the effectiveness of CoFGraph and demonstrate strong stability across varying training label ratios, with particularly consistent gains in low-resource regimes.

Temporal evolution attribute graph prediction, a key task in graph machine learning, aims to forecast the dynamic evolution of node attributes over time. While recent advances in Large Language Models (LLMs) have enabled their use in enhancing node representations for integration with Graph Neural Networks (GNNs), their potential to directly perform GNN-like aggregation and interaction remains underexplored. Furthermore, traditional approaches to initializing attribute embeddings often disregard structural semantics, limiting the provision of rich prior knowledge to GNNs. Current methods also primarily focus on 1-hop neighborhood aggregation, lacking the capability to capture complex structural interactions. To address these limitations, we propose a novel prediction framework that integrates structural information into attribute embeddings through the introduction of an attribute embedding loss. We design specialized prompts to enable LLMs to perform GNN-like aggregation and incorporate a relation-aware Graph Convolutional Network to effectively capture long-range and complex structural dependencies. Extensive experiments on multiple real-world datasets validate the effectiveness of our approach, demonstrating significant improvements in predictive performance over existing methods.

In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only consider the structural information of the subgraph and neglect the loss of semantic information caused by replacing entities with nodes. To address this problem, we propose a novel Commonsense Subgraph Meta-Learning (CSML) model. Specifically, we extract concepts from entities, which can be viewed as high-level semantic information. Unlike previous methods, we use concepts instead of nodes to construct commonsense subgraphs. By combining these with structural subgraphs, we can leverage both structural and semantic information for more comprehensive and rational predictions. Furthermore, we regard concepts as meta-information and employ meta-learning to facilitate rapid knowledge transfer, thus addressing more complex few-shot scenarios. Experimental results confirm the superior performance of our model in both standard and few-shot inductive reasoning.