arXiv:2602.23135v2 Announce Type: replace Abstract: Edge classification on directed dynamic graphs requires modeling interactions between source and destination nodes exhibiting asymmetrical behavioral patterns and temporal dynamics. However, existing dynamic graph architectures largely rely on shared parameters for processing source and destination nodes, with limited or no systematic role-aware modeling. We propose DyGnROLE (Dynamic Graph Node-Role-Oriented Latent Encoding), a Transformer-based architecture that disentangles source and destination representations. By using separate embedding
The increasing complexity and scale of dynamic graph data in AI applications necessitate more sophisticated models to capture intricate relationships and temporal dependencies.
This research advances the capability of AI models to understand and predict interactions in dynamic, directed networks, which are ubiquitous in social media, financial transactions, and cybersecurity.
The explicit disentanglement of source and destination representations via DyGnROLE allows for more accurate and nuanced modeling of asymmetric interactions and evolving roles within graphs.
- · AI researchers
- · Social network analysis platforms
- · Fraud detection systems
- · Intelligence agencies
- · Legacy graph neural networks
- · Generic AI modeling approaches
Improved accuracy in tasks like anomaly detection and link prediction on dynamic graphs.
Accelerated development of more adaptive and context-aware AI agents for real-world applications.
Enhanced AI capabilities contributing to more sophisticated autonomous systems operating in complex, dynamic environments.
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Read at arXiv cs.LG