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.

Mobile crowd sensing (MCS) is an emerging approach to collect data using smart devices. In MCS, task assignment is described as assigning existing tasks to known workers outside the constraints of task demand attributes and worker attributes, and maximizing the profit of the platform. However, workers and tasks often exist in different environments and heterogeneous features such as workers with attributes are not considered, leading to nondeterministic polynomial (NP)-hard task assignment problems. To optimize such problems, this article proposes a multiattribute environments-classes, agents, roles, groups, and objects (E-CARGO) task assignment model based on adaptive heterogeneous residual networks (AHRNets). The AHRNet is integrated into deep reinforcement learning (DRL) to optimize the NP-hard problem, dynamically adjust task assignment decisions and learn the relationship between workers with different attributes and task requirements. Multiattribute E-CARGO uses group task assignment policy to obtain the ideal worker-task assignment relationship. Compared with traditional heuristic algorithms for solving NP-hard, this method has the flexibility and applicability of adaptive networks, enabling the solver to interact with and adapt to new environments and generalize its experience to different situations. Under various experimental conditions, a large number of numerical results show that this method can achieve better results than the reference scheme.