{"title":"二部图上的分布(α, β)-核分解","authors":"Qing Liu, Xuankun Liao, Xinfeng Huang, Jianliang Xu, Yunjun Gao","doi":"10.1109/ICDE55515.2023.00075","DOIUrl":null,"url":null,"abstract":"(α, β)-core is an important cohesive subgraph model for bipartite graphs. Given a bipartite graph G, the problem of (α, β)-core decomposition is to compute non-empty (α, β)-cores for all possible values of α and β. The state-of-the-art (α, β)-core decomposition algorithm is a peeling-based algorithm, which iteratively deletes the vertex from high degree to low degree. However, as the peeling-based algorithm is designed for centralized environments, it cannot be applied to distributed environments, where graphs are partitioned and stored in different machines. Motivated by this, in this paper, we study the distributed (α, β)-core decomposition problem, aiming to develop new algorithms to support (α, β)-core decomposition in distributed environments. To this end, first, we analyze the local properties of (α, β)-core, and devise n-order Bi-indexes for the vertex, which are iteratively defined using the vertex neighbors’ (n − 1)-order Bi-indexes. Next, we propose an algorithm for (α, β)-core decomposition through iteratively calculating n-order Bi-indexes for every vertex. To further improve the efficiency of the algorithm, we propose two optimizations. Then, we extend our proposed algorithms to different distributed graph processing frameworks to make them run in distributed environments. Finally, extensive experimental results on both real and synthetic bipartite graphs demonstrate the efficiency of our proposed algorithms.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed (α, β)-Core Decomposition over Bipartite Graphs\",\"authors\":\"Qing Liu, Xuankun Liao, Xinfeng Huang, Jianliang Xu, Yunjun Gao\",\"doi\":\"10.1109/ICDE55515.2023.00075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"(α, β)-core is an important cohesive subgraph model for bipartite graphs. Given a bipartite graph G, the problem of (α, β)-core decomposition is to compute non-empty (α, β)-cores for all possible values of α and β. The state-of-the-art (α, β)-core decomposition algorithm is a peeling-based algorithm, which iteratively deletes the vertex from high degree to low degree. However, as the peeling-based algorithm is designed for centralized environments, it cannot be applied to distributed environments, where graphs are partitioned and stored in different machines. Motivated by this, in this paper, we study the distributed (α, β)-core decomposition problem, aiming to develop new algorithms to support (α, β)-core decomposition in distributed environments. To this end, first, we analyze the local properties of (α, β)-core, and devise n-order Bi-indexes for the vertex, which are iteratively defined using the vertex neighbors’ (n − 1)-order Bi-indexes. Next, we propose an algorithm for (α, β)-core decomposition through iteratively calculating n-order Bi-indexes for every vertex. To further improve the efficiency of the algorithm, we propose two optimizations. Then, we extend our proposed algorithms to different distributed graph processing frameworks to make them run in distributed environments. Finally, extensive experimental results on both real and synthetic bipartite graphs demonstrate the efficiency of our proposed algorithms.\",\"PeriodicalId\":434744,\"journal\":{\"name\":\"2023 IEEE 39th International Conference on Data Engineering (ICDE)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 39th International Conference on Data Engineering (ICDE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE55515.2023.00075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE55515.2023.00075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distributed (α, β)-Core Decomposition over Bipartite Graphs
(α, β)-core is an important cohesive subgraph model for bipartite graphs. Given a bipartite graph G, the problem of (α, β)-core decomposition is to compute non-empty (α, β)-cores for all possible values of α and β. The state-of-the-art (α, β)-core decomposition algorithm is a peeling-based algorithm, which iteratively deletes the vertex from high degree to low degree. However, as the peeling-based algorithm is designed for centralized environments, it cannot be applied to distributed environments, where graphs are partitioned and stored in different machines. Motivated by this, in this paper, we study the distributed (α, β)-core decomposition problem, aiming to develop new algorithms to support (α, β)-core decomposition in distributed environments. To this end, first, we analyze the local properties of (α, β)-core, and devise n-order Bi-indexes for the vertex, which are iteratively defined using the vertex neighbors’ (n − 1)-order Bi-indexes. Next, we propose an algorithm for (α, β)-core decomposition through iteratively calculating n-order Bi-indexes for every vertex. To further improve the efficiency of the algorithm, we propose two optimizations. Then, we extend our proposed algorithms to different distributed graph processing frameworks to make them run in distributed environments. Finally, extensive experimental results on both real and synthetic bipartite graphs demonstrate the efficiency of our proposed algorithms.