Deep Graph Generators: A Survey. - Faezeh Faez; Yassaman Ommi; Mahdieh Soleymani Baghshah; Hamid R. Rabiee (IEEE Access, 2021)

DMNP: A Deep Learning Approach for Missing Node Prediction in Partially Observed Graphs. - Faezeh Faez; Ali Akhoondian Amiri; Mahdieh Soleymani Baghshah; Hamid R. Rabiee (ASONAM, 2022)

CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation. - Yassaman Ommi; Matin Yousefabadi; Faezeh Faez; Amirmojtaba Sabour; Mahdieh Soleymani Baghshah; Hamid R. Rabiee (WWW (Companion Volume), 2022)

SCGG: A Deep Structure-Conditioned Graph Generative Model. - Faezeh Faez; Negin Hashemi Dijujin; Mahdieh Soleymani Baghshah; Hamid R. Rabiee (CoRR, 2022)

Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers. - Raika Karimi; Faezeh Faez; Yingxue Zhang 001; Xing Li; Lei Chen 031; Mingxuan Yuan; Mahdi Biparva (CoRR, 2024)

Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization. - Mahdi Biparva; Raika Karimi; Faezeh Faez; Yingxue Zhang 001 (Trans. Mach. Learn. Res., 2024)

MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization. - Faezeh Faez; Raika Karimi; Yingxue Zhang 001; Xing Li; Lei Chen 031; Mingxuan Yuan; Mahdi Biparva (ASP-DAC, 2025)