改进的k-core社区搜索方法
Improved Methods for k-core Community Search
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中文总结 AI 辅助
针对大规模网络中的多顶点社区搜索，提出基于k-core的SteinerKCore算法及并行ShellStruct构建方法，在Icebug中实现，较现有工具更高效可扩展，可处理数十亿边网络。
中文摘要 AI 辅助
基于用户指定查询节点的社区搜索是对社区发现或图聚类的补充。先前的社区搜索工作分为优化外部分离性或内部凝聚性，这些方法在超过十亿条边的网络上扩展性不佳。我们提出了SteinerKCore，一种新的基于k-core的可扩展社区搜索算法，用于多顶点查询。我们还提出了Par-ShellStruct，一种用于构建k-core社区搜索所用ShellStruct数据结构的并行算法。我们展示了在Icebug（一个用于大规模网络分析的开源工具包）中的实现，比对比工具更高效且更具可扩展性，能够在仅使用64GB RAM和16个CPU、运行时间不到4小时的情况下，在包含2.73亿条边和51亿条边的基准网络上执行。
英文摘要
Community search based on user-specified query nodes is complementary to community finding or graph clustering. Prior work in community search is divided into optimizing for external separate- ness or internal cohesiveness, which does not scale well networks of over a billion edges. We present SteinerKCore, a new scalable k-core based community search algorithm for multi-vertex queries. We also present Par-ShellStruct, a parallel algorithm for building the ShellStruct data structure used for k-core community search. We show that our implemen- tations in Icebug, an open-source toolkit for large-scale network analysis, are both more efficient and more scalable than comparative tools, being able to perform on a benchmark network of 273M and 5.1B edges using just 64GB RAM and under 4 hours runtime with 16 CPUs.
