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软件学报 ISSN 1000-9825, CODEN RUXUEW                                        E-mail: jos@iscas.ac.cn
                 Journal of Software,2024,35(6):2999−3012 [doi: 10.13328/j.cnki.jos.006926]  http://www.jos.org.cn
                 ©中国科学院软件研究所版权所有.                                                          Tel: +86-10-62562563



                                                        *
                 面向超图数据的最大独立集算法

                 徐兰天  1 ,    李荣华  1 ,    戴永恒  2 ,    王国仁  1


                 1
                  (北京理工大学 计算机学院, 北京 100081)
                 2
                  (电科云北京科技有限公司, 北京 100043)
                 通信作者: 李荣华, E-mail: lironghuabit@126.com

                 摘 要: 超图是普通图的泛化表示, 在许多应用领域都很常见, 包括互联网、生物信息学和社交网络等. 独立集问
                 题是图分析领域的一个基础性研究问题, 传统的独立集算法大多都是针对普通图数据, 如何在超图数据上实现高
                 效的最大独立集挖掘是一个亟待解决的问题. 针对这一问题, 提出一种超图独立集的定义. 首先分析超图独立集搜
                 索的两个特性, 然后提出一种基于贪心策略的基础算法. 接着提出一种超图近似最大独立集搜索的剪枝框架即精
                 Key words:  hypergraph; maximum independent set; heuristic algorithm
                 确剪枝与近似剪枝相结合, 以精确剪枝策略缩小图的规模, 以近似剪枝策略加快搜索速度. 此外, 还提出                               4  种高效
                 的剪枝策略, 并对每种剪枝策略进行理论证明. 最后, 通过在                10  个真实超图数据集上进行实验, 结果表明剪枝算法
                 可以高效地搜索到更接近于真实结果的超图最大独立集.
                 关键词: 超图; 最大独立集; 启发式算法
                 中图法分类号: TP311

                 中文引用格式: 徐兰天, 李荣华, 戴永恒, 王国仁. 面向超图数据的最大独立集算法. 软件学报, 2024, 35(6): 2999–3012. http://www.
                 jos.org.cn/1000-9825/6926.htm
                 英 文 引 用 格 式 : Xu  LT,  Li  RH,  Dai  YH,  Wang  GR.  Maximum  Independent  Set  Algorithm  for  Hypergraph  Data.  Ruan  Jian  Xue
                 Bao/Journal of Software, 2024, 35(6): 2999–3012 (in Chinese). http://www.jos.org.cn/1000-9825/6926.htm

                 Maximum Independent Set Algorithm for Hypergraph Data
                          1
                                      1
                                                   2
                 XU Lan-Tian , LI Rong-Hua , DAI Yong-Heng , WANG Guo-Ren 1
                 1
                 (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)
                 2
                 (Diankeyun Technologies Co. Ltd., Beijing 100043, China)
                 Abstract:  Hypergraphs  are  generalized  representations  of  ordinary  graphs,  which  are  common  in  many  application  areas,  including  the
                 Internet,  bioinformatics,  and  social  networks.  The  independent  set  problem  is  a  fundamental  research  problem  in  the  field  of  graph
                 analysis.  Most  of  the  traditional  independent  set  algorithms  are  targeted  for  ordinary  graph  data,  and  how  to  achieve  efficient  maximum
                 independent  set  mining  on  hypergraph  data  is  an  urgent  problem  to  be  solved.  To  address  this  problem,  this  study  proposes  a  definition  of
                 hypergraph  independent  sets.  Firstly,  two  properties  of  hypergraph  independent  set  search  are  analyzed,  and  then  a  basic  algorithm  based
                 on  the  greedy  strategy  is  proposed.  Then  a  pruning  framework  for  hypergraph  approximate  maximum  independent  set  search  is  proposed,
                 i.e.,  a  combination  of  exact  pruning  and  approximate  pruning,  which  reduces  the  size  of  the  graph  by  the  exact  pruning  strategy  and
                 speeds  up  the  search  by  the  approximate  pruning  strategy.  In  addition,  four  efficient  pruning  strategies  are  proposed  in  this  study,  and  a
                 theoretical  proof  of  each  pruning  strategy  is  presented.  Finally,  experiments  are  conducted  on  10  real  hypergraph  data  sets,  and  the  results
                 show that the pruning algorithm can efficiently search for hypergraph maximum independent sets that are closer to the real results.



                    现实世界中的实体关系大多不能用简单地二元关系来表示, 而超图能更好地表示实体间的多元关系. 超图作
                 为一种比普通图承载更多信息, 也有更强大的表现能力的图表示形式, 有着重要的研究价值. 超图和普通图有明显


                 *    基金项目: 国家重点研发计划  (2021YFB3301301); 国家自然科学基金  (U2241211, 62072034)
                  收稿时间: 2022-06-21; 修改时间: 2022-08-28, 2022-11-10; 采用时间: 2023-03-05; jos 在线出版时间: 2023-08-23
                  CNKI 网络首发时间: 2023-08-28
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