Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
cs.LG
Submitted: 2026-05-25
Updated: 2026-09-12
Comments: The simulation results are incorrect and could be misleading to the readers
Code: https://github.com/incoder-mru/Historical-Context-Integration-Module
License: http://creativecommons.org/licenses/by/4.0/
The gist: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and
Terminology
Abstract
Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks. While graph neural networks (GNNs) perform well for static or unsigned link prediction, effective learning in temporal signed graphs remains challenging due to the interaction of signed relations, evolving structure, and balance-theoretic constraints. To address this gap, we propose a modular temporal enhancement framework for signed GNNs that integrates historical context into otherwise static architectures. The framework introduces a Historical Context Integration Module (HCIM) that combines learnable recency-aware temporal weighting, LSTM-based embedding trajectory modeling, and multi-head temporal attention to capture both short- and long-term signed interaction dynamics. Historical information is fused with current node representations using either global or node-adaptive weighting, allowing the architecture-agnostic framework to accommodate heterogeneous temporal behaviors. We instantiate the approach on the Self-Explainable Signed Graph Transformer (SE-SGformer), preserving interpretability while extending it with temporal awareness. Experiments on real-world and synthetic TSNs, including Bitcoin OTC, Bitcoin Alpha, Reddit, and small-world network models, demonstrate consistent and statistically significant improvements over the static baseline.
Sources
- Temporal Graph Networks for Deep Learning on Dynamic Graphs
- Semi-Supervised Classification with Graph Convolutional Networks
- Graph Attention Networks
- Neural Machine Translation by Jointly Learning to Align and Translate
- Decoupled Weight Decay Regularization
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