从数据库网络中发现主题社区
Finding Theme Communities from Database Networks
摘要：
给定一个数据库网络，其中每个顶点都与一个事务数据库相关联，我们感兴趣的是寻找主题社区。这里，主题社区是一个内聚子图，使得公共模式在与子图中的顶点相关联的所有事务数据库中是频繁的。从数据库网络中寻找所有主题社区有许多新颖的应用。然而，这是具有挑战性的，因为即使计算数据库网络中所有主题社区的数量也是#P-hard。受观察到的主题社区收缩时，模式的长度增加的启发，我们调查的主题社区的几个属性和开发TCFI，一个可扩展的算法，使用这些属性，有效地修剪模式，不能形成任何主题社区。我们还设计了TC-Tree，一个可扩展的算法，有效地分解和索引的主题社区。从一个包含数亿个主题社区的TC树中检索主题社区的排名列表需要不到1秒的时间。大量的实验和案例研究表明，TCFI和TC-Tree在发现和查询有意义的主题社区从大型数据库网络的有效性和可扩展性。
英文摘要：
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
