MoDiff - Graph Generation with Motif-aware Diffusion Model

Temporal graphs, widely used in social network modeling, are valuable for research but pose challenges due to data accessibility and privacy concerns. High-quality graph generation models can produce surrogate data for sharing and training, benefiting tasks such as behavior analysis, anomaly detection, and data augmentation. However, existing deep learning and probabilistic approaches often struggle to balance global statistical properties with local structural details. To overcome this limitation, we leverage motifs, small subgraphs that serve as the building blocks of complex networks, to encode local information. Based on a spectral analysis of motifs, we propose MoDiff, a novel motif-aware diffusion model for temporal graph generation. MoDiff integrates motifs into a diffusion framework by employing motif-enhanced Hermitian matrices that capture local structures and edge orientations, while the spectral diffusion model efficiently generates graphs. Moreover, MoDiff supports controllable graph generation by adjusting density parameters to simulate the evolution of temporal graphs. Experimental results demonstrate that MoDiff outperforms existing approaches, reducing degree discrepancies by 10-50% and clustering discrepancies by 50-90%, while better preserving higher-order structural features. Our code is available at: https://github.com/Yuwe1XU/MoDiff.