Topology‐aware tensor decomposition for meta‐graph learning - Hansi Yang; Quanming Yao (CAAI Transactions on Intelligence Technology, 2025)
Abstract Heterogeneous graphs generally refer to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use meta‐graphs, which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph. However, how to design proper meta‐graphs is challenging. Recently, there have been many works on learning suitable meta‐graphs from a heterogeneous graph. Existing methods generally introduce continuous weights for edges that are independent of each other, which ignores the topological structures of meta‐graphs and can be ineffective. To address this issue, the authors propose a new viewpoint from tensor on learning meta‐graphs. Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC (CP) decomposition, but also inspires us to propose a topology‐aware tensor decomposition, called TENSUS, that reflects the structure of DAGs. The proposed topology‐aware tensor decomposition is easy to use and simple to implement, and it can be taken as a plug‐in part to upgrade many existing works, including node classification and recommendation on heterogeneous graphs. Experimental results on different tasks demonstrate that the proposed method can significantly improve the state‐of‐the‐arts for all these tasks.