MPRW-HGNN: A Meta-path Random Walk based Heterogeneous Graph Neural Network

In recent years, graph neural networks have been widely used in multimedia information retrieval and other graph data processing tasks due to their powerful feature learning capabilities. However, existing graph neural network models typically handle only homogeneous graph data with simple structures and single semantics effectively. However, when faced with heterogeneous multimedia data characterized by complex interactions and rich semantics, the performance of traditional models degrades significantly. To address this challenge, this paper proposes a heterogeneous graph neural network algorithm based on meta-path random walk (MPRW-HGNN). First, we design a module at the meta-path instance level that generates meta-path instances and their structural representations via random walks. Then, a soft-attention mechanism is employed to fuse information from multiple meta-paths, better capturing the fine-grained semantic structures around nodes. Subsequently, we utilize a self-attention mechanism to explore semantic correlations and differences between multiple paths, enabling adaptive weighted fusion of multiple path information to generate robust node features for heterogeneous graph data. Through extensive experiments on information retrieval tasks using the IMDB movie dataset and DBLP academic paper dataset, we demonstrate the significant advantages of our proposed algorithm in handling multimodal data and improving retrieval accuracy.