Temporal Relations-Aware Nonnegative Latent Factorization of Tensors for Dynamic Directed Graph Representation
提出一种时序关系感知的非负张量潜因子模型，通过RESCAL分解和自适应参数学习，更精确地表示动态有向图中的节点和时序交互，在八个真实数据集上提升了表示精度和计算效率。
Dynamic directed graphs are increasingly used to model complex relational systems across various domains, such as telecommunication networks. Latent factorization of tensors (LFoT) models are effective for dynamic graph representation learning. However, existing LFoT models typically rely on canonical polyadic decomposition and fail to capture the unipartite characteristics of dynamic directed graphs, i.e.,such graphs concern the temporal interactions among a single node set, thereby limiting the node representation accuracy and temporal interaction modeling capability. To address this critical issue in system modeling, this article proposes a novel temporal relations-aware nonnegative LFoT (TRNL) model whose ideas are threefold: 1)building a nonnegative RESCAL-based LFoT model for representing the nodes and unipartite temporal interactions precisely; 2)developing an adaptive parameter-learning scheme to improve training efficiency and practical applicability; and 3)theoretically proving that its convergence is guaranteed under the proposed learning scheme. Experimental results on eight real-world dynamic directed graphs demonstrate that TRNL outperforms state-of-the-art baselines in both representation learning accuracy and computational efficiency.