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We demonstrate PrismX for its (1) ease of programming by reusing PRAM algorithms, (2) efficiency by comparing with the state-of-the-art graph systems, single-machine or multi-machine, in-memory or out-of-core; (3) parallel scalability of in-memory PRAM algorithms, reducing runtime when more CPU cores are available; and (4) applications in credit risk assessment.<\/jats:p>","DOI":"10.14778\/3685800.3685906","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4485-4488","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PrismX: A Single-Machine System for Querying Big Graphs"],"prefix":"10.14778","volume":"17","author":[{"given":"Shuhao","family":"Liu","sequence":"first","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Wenfei","family":"Fan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, China and University of Edinburgh, UK and Beihang University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Gluon: A Communication-Optimizing Substrate for Distributed Heterogeneous Graph Analytics. 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