{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:41:46Z","timestamp":1768995706743,"version":"3.49.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>Due to diverse graph data and algorithms, programming and orchestration of complex computation pipelines have become the major challenges to making use of graph applications for Web-scale data analysis. GraphScope aims to provide a one-stop and efficient solution for a wide range of graph computations at scale. It extends previous systems by offering a unified and high-level programming interface and allowing the seamless integration of specialized graph engines in a general data-parallel computing environment.<\/jats:p>\n          <jats:p>As we will show in this demo, GraphScope enables developers to write sequential graph programs in Python and provides automatic parallel execution on a cluster. This further allows GraphScope to seamlessly integrate with existing data processing systems in PyData ecosystem. To validate GraphScope's efficiency, we will compare a complex, multi-staged processing pipeline for a real-life fraud detection task with a manually assembled implementation comprising multiple systems. GraphScope achieves a 2.86\u00d7 speedup on a trillion-scale graph in real production at Alibaba.<\/jats:p>","DOI":"10.14778\/3476311.3476324","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:56Z","timestamp":1635461336000},"page":"2703-2706","source":"Crossref","is-referenced-by-count":6,"title":["GraphScope"],"prefix":"10.14778","volume":"14","author":[{"given":"Jingbo","family":"Xu","sequence":"first","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Zhanning","family":"Bai","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Wenfei","family":"Fan","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Longbin","family":"Lai","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Zhengping","family":"Qian","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Yanyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Wenyuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group and University of Edinburgh"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Gremlin Apache Tinkerpop. 2015. https:\/\/tinkerpop.apache.org\/gremlin.html  Gremlin Apache Tinkerpop. 2015. https:\/\/tinkerpop.apache.org\/gremlin.html"},{"key":"e_1_2_1_2_1","unstructured":"Dask Development Team. 2016. https:\/\/dask.org  Dask Development Team. 2016. https:\/\/dask.org"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035942"},{"key":"e_1_2_1_4_1","unstructured":"Apache Spark GraphX. 2014. https:\/\/spark.apache.org\/graphx\/  Apache Spark GraphX. 2014. https:\/\/spark.apache.org\/graphx\/"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331372"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807184"},{"key":"e_1_2_1_7_1","unstructured":"Koalas Project. 2020. https:\/\/github.com\/databricks\/koalas  Koalas Project. 2020. https:\/\/github.com\/databricks\/koalas"},{"key":"e_1_2_1_8_1","volume-title":"GAIA: A System for Interactive Analysis on Distributed Graphs Using a High-Level Language. 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