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DTSTAMP:20240626T180033Z
LOCATION:3010\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240626T104500
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UID:dac_DAC 2024_sess142_RESEARCH1537@linklings.com
SUMMARY:inGRASS: Incremental Graph Spectral Sparsification via Low-Resista
 nce-Diameter Decomposition
DESCRIPTION:Research Manuscript\n\nAli Aghdaei and Zhuo Feng (Stevens Inst
 itute of Technology)\n\nThis work presents inGRASS, a novel algorithm desi
 gned for incremental spectral sparsification of large undirected graphs. T
 he proposed inGRASS algorithm is highly scalable and parallel-friendly, ha
 ving a nearly linear time complexity for the setup phase and the ability t
 o update the spectral sparsifier in $O(\log N)$ time for each incremental 
 change made to the original graph with $N$ nodes. A key component in the s
 etup phase of inGRASS is a multilevel resistance embedding step for effici
 ently identifying spectrally critical edges and effectively pruning spectr
 ally similar ones, which is achieved by decomposing the initial sparsifier
  into node clusters with bounded effective-resistance diameters achieved t
 hrough a low-resistance-diameter decomposition (LRD) scheme.  The update p
 hase of inGRASS exploits low-dimensional node embedding vectors for effici
 ently estimating the importance and uniqueness of each newly added edge. A
 s demonstrated through extensive experiments, inGRASS achieves state-of-th
 e-art results in incremental spectral sparsification of graphs obtained fr
 om various tasks, such as circuit simulations, finite element analysis, an
 d social networks.\n\nTopic: EDA\n\nKeyword: Analog CAD, Simulation, Verif
 ication and Test\n\nSession Chairs: Weidong Cao (Washington University, St
 . Louis) and Ahmet Budak (Analog Devices, Inc. (ADI))
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