Spectral-temporal dual view graph convolutional networks: A deep learning framework for dynamic bipartite graphs.

Dynamic bipartite graphs (DBGs) are widely used in real-world scenarios, where representation learning is particularly challenging due to the heterogeneity of node types and the temporal evolution of interactions. A key difficulty lies in jointly capturing non-stationary micro-level preference dynamics and macro-level stable structural evolution. Existing methods typically focus on either temporal dependency modeling or structural pattern extraction. For instance, recurrent or attention-based temporal models struggle to capture abrupt, short-term preference shifts, while spectral or structure-oriented approaches emphasize long-term regularities but overlook fine-grained dynamic variations. This imbalance becomes especially pronounced in scenarios with sparse interactions or sudden behavioral changes. To address these challenges, this paper proposes a spectral-temporal dual-view framework, ST-DYB, for DBG representation learning. By explicitly decoupling macro-level structural evolution and micro-level preference dynamics, the model captures complementary heterogeneous patterns from two perspectives. A cross-view adaptive fusion mechanism further integrates these representations, enabling effective modeling of both implicit preference dependencies and temporal dynamics. Extensive experiments on multiple real-world datasets demonstrate that ST-DYB consistently outperforms state-of-the-art methods in dynamic link prediction and node classification tasks, validating the effectiveness and robustness of the proposed dual-view modeling paradigm.