| 二部图凝聚子图快速查询算法以及在电子商务中的应用 |
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| 引用本文：张文超1,何逸章2,王丽苹3.二部图凝聚子图快速查询算法以及在电子商务中的应用[J].计算技术与自动化,2021,(2):154-158 |
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| 中文摘要:随着互联网的飞速发展，亚马逊，阿里巴巴和eBay这样的的电子商务平台已经成为世界经济不可或缺的一环。在这些电子商务平台中，用户和商品之间的互动可以自然地抽象成二部图，其中每个点表示用户或商品，每条边表示用户购买或评价了物品。如果一些用户和商品之间发生了紧密的联系，那么他们就形成了一个电子社区。基于二部图中的凝聚子图模型(α,β）-core，引入了 (α,β)-组的概念来代表社区。设计了有效且快速的算法来计算大规模用户-商品二部图中包含给定查询点的 (α,β)-组，给出了查询算法并分析了算法的时间和空间复杂度。在6个真实数据集上的实验证实了采用 (α,β)-组这一模型的合理性以及提出的算法的高效性。 |
| 中文关键词:凝聚子图计算 电子商务 社区搜索 二部图 |
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| Efficient Algorithms for Cohesive Subgraph Search in Bipartite Graphs and Applications on E-commerce |
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| Abstract:With the rapid development of the Internet, e-commerce platforms like Amazon, Alibaba, and eBay have become an integral part of the world economy. The interactions between users and products on such platforms can be naturally modeled as a bipartite graph, in which each node represents a user, or a product and each edge indicates that a user has bought or reviewed a product. If some users and products are closely connected, they form a community in such an e-commerce network. In this paper, based on the cohesive subgraph model (α,β)-core in bipartite graphs, (α,β)-group is proposed to represent these communities. Effective and efficient algorithms are devised to compute the (α,β)-group containing the query vertex on large-scale user-product network. The space and time complexities of the algorithms are analyzed in detail. Experiments on 6 real datasets validate the effectiveness of (α,β)-group and the efficiency of the proposed algorithms. |
| keywords:cohesive subgraph computation e-commerce community search bipartite graph |
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