Heterogeneous graphs (HGs) are graph topologies with multiple types of nodes and edges, which have attracted increasing attention in graph representation learning. Many heterogeneous graph neural networks (HGNNs) assume that the graph structure information is complete when processing heterogeneous graph data. However, due to factors such as privacy protection and high data acquisition costs, heterogeneous graphs often suffer from missing attributes. Existing methods that rely on local neighbor aggregation to obtain missing attributes have achieved good performance in various downstream tasks, yet when neighbor types differ from the target node type, the completed attributes become highly unreliable. Therefore, this paper proposes a novel method for Attribute Completion in Heterogeneous Graph with Integration of External Knowledge from Large Language Models (HGACLLM). HGACLLM simplifies local attribute aggregation while maintaining model performance, and is the first to leverage LLMs to learn external knowledge of nodes with missing attributes in the attribute completion task on HGs. First, HGACLLM uses a mean aggregator to precalculate neighbor attributes along different metapaths and applies a Transformer-based semantic fusion module to integrate node attributes from various metapaths, generating local attributes. Second, it infers implicit external knowledge of target nodes based on textual descriptions of attribute-rich neighbors along metapaths. Finally, HGACLLM combines local attributes and external knowledge to generate complete attribute representations. We evaluated our method on six real-world datasets, where HGACLLM achieved up to a 1.94% improvement in evaluation metrics for the downstream tasks. The experimental results demonstrate that HGACLLM achieves state-of-the-art performance.

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% ( 22.7% ). The code is available via https://github.com/ShenzhiYang2000/NODESAFE-Bounded-and-Uniform-Energy-based-Out-of-distribution-Detection-for-Graphs.