Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks

2026-05-25

AI Summary

This work addresses the challenges posed by the dynamic evolution of cooperative and antagonistic relationships, structural changes, and balance-theoretic constraints in temporal signed networks, which existing graph neural networks struggle to capture. The authors propose an architecture-agnostic, modular temporal augmentation framework that injects temporal information into static signed graph neural networks via a Historical Context Integration Module (HCIM). This module combines learnable time-decay weights, LSTM-based trajectory embeddings, and multi-head temporal attention to enable either node-adaptive or global fusion of historical context. While preserving model interpretability, the approach flexibly captures heterogeneous temporal behaviors and achieves significant performance gains over static baselines on dynamic link prediction across multiple datasets, including Bitcoin OTC, Bitcoin Alpha, Reddit, and small-world networks.

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 \emph{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.

Problem

• Temporal signed networks
• Dynamic link prediction
• Signed graph neural networks
• Balance theory
• Temporal dynamics

Innovation

• Temporal Signed Networks
• Graph Neural Networks
• Historical Context Integration
• Dynamic Link Prediction
• Modular Temporal Enhancement