Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation

Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation Tao Yin a, Chen Zhao b, Xiaoyan Liu c and Minglai Shao a, a School of New Media and Communication, Tianjin University, Tianjin, China b Department of Computer Science, Baylor University, Texas, USA c School of Qiyue Media and Communication, Cangzhou Normal University, Hebei, ChinaA R T I C L E I N F OKeywords: Heterogeneous Graph Out-of-distribution Detection Energy A B S T R A C T Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification tasks within in-distribution (ID) settings, real-world scenarios often present distribution shifts, leading to the presence of out-of-distribution (OOD) nodes. OOD detection in graphs is a crucial and challenging task. Most existing research focuses on homogeneous graphs, but real-world graphs are often heterogeneous, consisting of diverse node and edge types. This heterogeneity adds complexity and enriches the informational content. To the best of our knowledge, OOD detection in heterogeneous graphs remains an underexplored area. In this context, we propose a novel methodology for OOD detection in heterogeneous graphs (OODHG) that aims to achieve two main objectives: 1) detecting OOD nodes and 2) classifying all ID nodes based on the first task's results. Specifically, we learn representations for each node in the heterogeneous graph, calculate energy values to determine whether nodes are OOD, and then classify ID nodes. To leverage the structural information of heterogeneous graphs, we introduce a meta-path-based energy propagation mechanism and an energy constraint to enhance the distinction between ID and OOD nodes. Extensive experimental findings substantiate the simplicity and effectiveness of OODHG, demonstrating its superiority over baseline models in OOD detection tasks and its accuracy in ID node classification.1. Introduction The rapid progression of graph neural networks (GNNs) has profoundly impacted various domains, where graph data play a crucial role. GNNs can extract rich structural information from graphs. This enables them to effectively model complex relationships in graph data [25]. This capability has driven their widespread adoption across a diverse range of domains, including social networks, knowledge graphs, the world wide web, and numerous others.