Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Shi, Jessica"'
Autor:
Yu, Shangdi, Shi, Jessica, Meindl, Jamison, Eisenstat, David, Ju, Xiaoen, Tavakkol, Sasan, Dhulipala, Laxman, Łącki, Jakub, Mirrokni, Vahab, Shun, Julian
We introduce the ParClusterers Benchmark Suite (PCBS) -- a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing different graph clustering algorithms and implementations. The benchmark in
Externí odkaz:
http://arxiv.org/abs/2411.10290
Autor:
Venkatasubramanian, Venkat, Shi, Jessica, Goldman, Leo, M., Arun Sankar E., Sivaram, Abhishek
Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. more segregation. Here, we present a game-theoretic model that predicts s
Externí odkaz:
http://arxiv.org/abs/2403.04084
Autor:
Shi, Jessica
Large-scale graph processing is a fundamental tool in modern data mining, with wide-ranging applications in domains including social network analysis, bioinformatics, and machine learning. In particular, graph clustering, or community detection, is a
Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to gen
Externí odkaz:
http://arxiv.org/abs/2306.08623
Autor:
Dhulipala, Laxman, Liu, Quanquan C., Raskhodnikova, Sofya, Shi, Jessica, Shun, Julian, Yu, Shangdi
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between in
Externí odkaz:
http://arxiv.org/abs/2211.10887
Obtaining scalable algorithms for hierarchical agglomerative clustering (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficiently parallelizing HAC is difficult due to the seemingly sequential natu
Externí odkaz:
http://arxiv.org/abs/2206.11654
Autor:
Malecki, Sarah L., Loffler, Anne, Tamming, Daniel, Dyrby Johansen, Niklas, Biering-Sørensen, Tor, Fralick, Michael, Sohail, Shahmir, Shi, Jessica, Roberts, Surain B, Colacci, Michael, Ismail, Marwa, Razak, Fahad, Verma, Amol A.
Publikováno v:
In International Journal of Medical Informatics September 2024 189
This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is efficient both in theory and in practice. Our algorithm achieves a work co
Externí odkaz:
http://arxiv.org/abs/2111.10980
This document describes an attempt to develop a compiler-based approach for computations with symmetric tensors. Given a computation and the symmetries of its input tensors, we derive formulas for random access under a storage scheme that eliminates
Externí odkaz:
http://arxiv.org/abs/2110.00186
Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quali
Externí odkaz:
http://arxiv.org/abs/2108.01731