arXiv:2605.26290v1 Announce Type: new 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 fram
The increasing complexity and interconnectedness of real-world systems, from social networks to financial markets, necessitate more sophisticated analytical tools like GNNs to model and predict dynamic signed relationships.
Improved capabilities in dynamic link prediction within signed graphs will enhance applications in social media analysis, trust systems, and financial transaction network monitoring, impacting critical sectors.
The development of effective GNNs for temporal signed graphs allows for better understanding and prediction of evolving cooperative and adversarial relationships, overcoming previous limitations in static or unsigned graph analysis.
- · AI researchers and developers
- · Social media analytics companies
- · Financial fraud detection systems
- · Cybersecurity platforms
- · Traditional static graph analysis methods
- · Organizations relying on outdated network prediction models
More accurate predictions of social and financial network dynamics.
Enhanced ability to identify emerging threats or opportunities in complex interactive systems.
Potential for new forms of automated decision-making based on predictive network intelligence, influencing trust and reputation systems at scale.
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Read at arXiv cs.LG