PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph

• URL: http://arxiv.org/abs/2504.17641v2
• Date: Fri, 25 Apr 2025 03:38:56 GMT
• Title: PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph
• Authors: Shengtao Zhang, Haokai Zhang, Shiqi Lou, Zicheng Wang, Zinan Zeng, Yilin Wang, Minnan Luo,
• Abstract summary: Dynamic node classification is critical for modeling evolving systems like financial transactions and academic collaborations.<n>It is difficult to collect all dynamic labels in real-world scenarios due to high annotation costs and label uncertainty.<n>We propose PTCL, a pioneering method addressing label-limited dynamic node classification where only final labels are available.
• Score: 22.78831582526477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic node classification is critical for modeling evolving systems like financial transactions and academic collaborations. In such systems, dynamically capturing node information changes is critical for dynamic node classification, which usually requires all labels at every timestamp. However, it is difficult to collect all dynamic labels in real-world scenarios due to high annotation costs and label uncertainty (e.g., ambiguous or delayed labels in fraud detection). In contrast, final timestamp labels are easier to obtain as they rely on complete temporal patterns and are usually maintained as a unique label for each user in many open platforms, without tracking the history data. To bridge this gap, we propose PTCL(Pseudo-label Temporal Curriculum Learning), a pioneering method addressing label-limited dynamic node classification where only final labels are available. PTCL introduces: (1) a temporal decoupling architecture separating the backbone (learning time-aware representations) and decoder (strictly aligned with final labels), which generate pseudo-labels, and (2) a Temporal Curriculum Learning strategy that prioritizes pseudo-labels closer to the final timestamp by assigning them higher weights using an exponentially decaying function. We contribute a new academic dataset (CoOAG), capturing long-range research interest in dynamic graph. Experiments across real-world scenarios demonstrate PTCL's consistent superiority over other methods adapted to this task. Beyond methodology, we propose a unified framework FLiD (Framework for Label-Limited Dynamic Node Classification), consisting of a complete preparation workflow, training pipeline, and evaluation standards, and supporting various models and datasets. The code can be found at https://github.com/3205914485/FLiD.

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