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UID:dac_DAC 2026_sess317_RESEARCH2887@linklings.com
SUMMARY:HyperEFs: Adaptive Clique Sampling for Scalable Effective Resistan
 ce Estimation in Large Hypergraphs
DESCRIPTION:Hamed Sajadinia, Yihang Yuan, and Zhuo Feng (Stevens Institute
  of Technology)\n\nWe propose HyperEFs, a scalable framework for effective
  resistance estimation in large hypergraphs based on adaptive clique sampl
 ing. The proposed sampling strategy enables smooth transitions between sta
 r and clique expansions of hyperedges, substantially improving the quality
  of Krylov subspace embeddings. HyperEFs estimates pairwise effective-resi
 stance distances in large hypergraphs in nearly linear time and integrates
  seamlessly into state-of-the-art multilevel hypergraph partitioning frame
 works, yielding significantly improved solution quality. Extensive experim
 ents on VLSI benchmarks demonstrate up to 100× speedup over HyperEF 2.0 wi
 th greatly reduced memory usage. On large-scale hypergraph datasets such a
 s Titan23, our framework achieves superior cut sizes, demonstrating except
 ional runtime scalability and efficiency.\n\nTrack: EDA7-II. Physical Desi
 gn and Verification, Design\n\nSession Chairs: Christian Pilato (Politecni
 co di Milano); Mohsen Imani (University of California, Irvine); Flavio Pon
 zina (San Diego State University); Amin Farshidi (Cadence Design Systems, 
 Inc.); Yonggan Fu (Georgia Institute of Technology, NVIDIA); Dimitrios Tsa
 ras (Huawei, Hong Kong University of Science and Technology (HKUST)); Anh 
 Nguyen (Fujitsu Limited); Ruiyang Qin (Villanova University); Jiliang Zhan
 g (Hunan University); Meng Li (Peking University); and Rassul Bairamkulov 
 (Advanced Micro Devices (AMD))\n\n
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