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We propose a notion of graph joins to enrich<jats:italic>G<\/jats:italic><jats:sub>1<\/jats:sub>by aligning entities across<jats:italic>G<\/jats:italic><jats:sub>1<\/jats:sub>and<jats:italic>G<\/jats:italic><jats:sub>2<\/jats:sub>. We also introduce a graph filtering method to support graph joins, by fetching only the data of<jats:italic>G<\/jats:italic><jats:sub>2<\/jats:sub>that pertains to the entities of<jats:italic>G<\/jats:italic><jats:sub>1<\/jats:sub>, to reduce noise and the size of the fused data. Based on these we develop a parallel algorithm to discover GARs across<jats:italic>G<\/jats:italic><jats:sub>1<\/jats:sub>and<jats:italic>G<\/jats:italic><jats:sub>2<\/jats:sub>. Moreover, we provide an incremental GAR discovery algorithm in response to updates to<jats:italic>G<\/jats:italic><jats:sub>1<\/jats:sub>and<jats:italic>G<\/jats:italic><jats:sub>2<\/jats:sub>. We show that both algorithms guarantee to reduce parallel runtime when given more processors. Better yet, the incremental algorithm is bounded relative to the batch one. Using real-life and synthetic data, we empirically verify that the methods improve the accuracy of association analyses by 30.4% on average, and scale well with large graphs.<\/jats:p>","DOI":"10.14778\/3648160.3648162","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T21:52:53Z","timestamp":1714773173000},"page":"1173-1186","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Capturing More Associations by Referencing External Graphs"],"prefix":"10.14778","volume":"17","author":[{"given":"Wenfei","family":"Fan","sequence":"first","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, University of Edinburgh, Beihang University"}]},{"given":"Muyang","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Edinburgh"}]},{"given":"Shuhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences"}]},{"given":"Chao","family":"Tian","sequence":"additional","affiliation":[{"name":"Beihang University"}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2022. 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