UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs

• URL: http://arxiv.org/abs/2502.16431v1
• Date: Sun, 23 Feb 2025 04:11:21 GMT
• Title: UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs
• Authors: Yuanyuan Xu, Wenjie Zhang, Xuemin Lin, Ying Zhang,
• Abstract summary: We propose a unified representation learning approach for dynamic graphs.<n>We show that our UniDyG achieves an average improvement of 14.4% over sixteen baselines across nine dynamic graphs.
• Score: 25.66188469474215
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic graphs are formulated in continuous-time or discrete-time dynamic graphs. They differ in temporal granularity: Continuous-Time Dynamic Graphs (CTDGs) exhibit rapid, localized changes, while Discrete-Time Dynamic Graphs (DTDGs) show gradual, global updates. This difference leads to isolated developments in representation learning for each type. To advance representation learning, recent research attempts to design a unified model capable of handling both CTDGs and DTDGs. However, it typically focuses on local dynamic propagation for temporal structure learning in the time domain, failing to accurately capture the structural evolution associated with each temporal granularity. In addition, existing works-whether specific or unified-often overlook the issue of temporal noise, compromising the model robustness and effectiveness. To better model both types of dynamic graphs, we propose UniDyG, a unified and effective representation learning approach, which scales to large dynamic graphs. We first propose a novel Fourier Graph Attention (FGAT) mechanism that can model local and global structural correlations based on recent neighbors and complex-number selective aggregation, while theoretically ensuring consistent representations of dynamic graphs over time. Based on approximation theory, we demonstrate that FGAT is well-suited to capture the underlying structures in CTDGs and DTDGs. We further enhance FGAT to resist temporal noise by designing an energy-gated unit, which adaptively filters out high-frequency noise according to the energy. Last, we leverage our FGAT mechanisms for temporal structure learning and employ the frequency-enhanced linear function for node-level dynamic updates, facilitating the generation of high-quality temporal embeddings. Extensive experiments show that our UniDyG achieves an average improvement of 14.4% over sixteen baselines across nine dynamic graphs.

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