HALO-GNN: hallucination-resistant temporal graph neural networks for dynamic community detection

Temporal graph learning is inherently subject to illusory structural dynamics, which is a phenomenon in which transient noise and ephemeral perturbations are exaggerated into deceptive community movements. Consequently, this leads to community assignments that are unstable and inconsistent, which severely limits the effectiveness of temporal graph neural networks in situations that involve streaming and dynamic circumstances. We provide a learning paradigm that is resistant to hallucinations and stabilizes temporal representations through the use of memory-guided structural regularization. The framework that has been suggested stabilizes node embeddings by referencing historical structures. This helps to reduce oscillations that are brought on by high-frequency noise while also preserving low-frequency development that is compatible with the community. Thorough evaluations across a variety of temporal graph benchmarks demonstrate significant improvements in robustness, temporal consistency, and perturbation resilience. These findings highlight the importance of hallucination resistance for the purpose of achieving reliable dynamic community detection.