计算机应用 ›› 2019, Vol. 39 ›› Issue (3): 899-906.DOI: 10.11772/j.issn.1001-9081.2018071628
收稿日期:
2018-08-06
修回日期:
2018-09-02
出版日期:
2019-03-10
发布日期:
2019-03-11
通讯作者:
胡昕晨
作者简介:
卢志刚(1973-),男,湖北荆门人,教授,博士,主要研究方向:大数据分析、商务智能、供应链管理;胡昕晨(1994-),女,山东滕州人,硕士研究生,主要研究方向:大数据分析、商务智能。
基金资助:
上海市自然科学基金资助项目(18ZR1416900)。
Received:
2018-08-06
Revised:
2018-09-02
Online:
2019-03-10
Published:
2019-03-11
Supported by:
This work is partially supported by the Natural Science Foundation of Shanghai (18ZR1416900).
摘要:
针对现有企业社群发现算法多侧重于同质性市场环境,不能反映部分企业会参与多条供应链作业的问题,提出一种基于节点映射关系的核社群表示模型Map-Community,通过构塑两种角色节点及其相互间不同的映射关系,判断企业的社群归属问题。基于该表示模型提出一种具有近似线性阶时空复杂度的节点映射算法(NMA)。首先,采取过滤操作获得供应链网络拓扑图中的双连通核心图;然后,引入映射度择选出核心企业节点;其次,依据映射判断规则进行局部扩展;最后,通过回溯将局部社群结构拓展至全局网络并发现重叠区域。LFR网络应用实验中,NMA对阈值变化反映出低敏感性,且在实用性方面优于LFM、COPRA和GCE。在企业社交网络进行仿真,利用划分情况总结分布效应意义。实验结果验证了该算法对于企业重叠社群发现的可行性及其在发现质量方面的性能优势。
中图分类号:
卢志刚, 胡昕晨. 基于节点映射的核型企业重叠社群发现算法[J]. 计算机应用, 2019, 39(3): 899-906.
LU Zhigang, HU Xinchen. Discovery algorithm for overlapping enterprise community with kernel based on node mapping[J]. Journal of Computer Applications, 2019, 39(3): 899-906.
[1] GULBAHCE N, LEHMANN S. The art of community detection[J]. Bioessays, 2008, 30(10):934-938. [2] MUCHA P J, RICHARDSON T, MACON K, et al. Community structure in time dependent,multiscale, and multiplex networks[J]. Science, 2010, 328(5980):876-878. [3] 隆华,李宝安.基于重叠度与模块度增量的复杂网络社区识别[J].计算机应用,2017,37(S1):6-8.(LONG H, LI B A. Community identificaiton of complex networks based on overlapping degree and module increment[J]. Journal of Computer Applications, 2017, 37(S1):6-8.) [4] NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2):026113. [5] GIRVAN M, NEWMAN M E J. Community structure in social and biological networks[EB/OL].[2018-07-10]. https://arxiv.org/pdf/cond-mat/0112110.pdf. [6] DUCH J, ARENAS A. Community detection in complex networks using extremal optimization[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2005, 72(2 Pt 2):027104. [7] BOETTCHER S, PERCUS A G. Optimization with extremal dynamics[J]. Physical Review Letters, 2001, 86(23):5211-5214. [8] NEWMAN M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E, 2004, 69(6):066133. [9] CAPOCCI A, SERVEDIO V D P, CALDARELLI G, et al. Detecting communities in large networks[J]. Physica A:Statistical Mechanics and Its Applications, 2005, 352(2/3/4):669-676. [10] DONETTI L, MUÑOZ M A. Detecting network communities:a new systematic and efficient algorithm[EB/OL].[2018-07-11]. https://arxiv.org/pdf/cond-mat/0404652v2.pdf. [11] RAGHAVAN U N, ALBERT R, KUMARA S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2007, 76(3 Pt 2):036106. [12] 陈羽中,施松,朱伟平,等.一种基于邻域跟随关系的增量社区发现算法[J].计算机学报,2017,40(3):570-583.(CHEN Y Z, SHI S, ZHU W P, et al. An incremental community discovery algorithm based on neighborhood following relationship[J]. Chinese Journal of Computers, 2017, 40(3):570-583.) [13] ROSVALL M, BERGSTROM C T. Maps of random walks on complex networks reveal community structure[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(4):1118-1123. [14] 邓小龙,王柏,吴斌,等.基于信息熵的复杂网络社团划分建模和验证[J].计算机研究与发展,2012,49(4):725-734.(DENG X L, WANG B, WU B, et al. Modularity modeling and evaluation in community detecting of complex network based on information entropy[J]. Journal of Computer Research and Development, 2012, 49(4):725-734.) [15] PALLA G, DERÉNYI I, FARKAS I, et al. Uncovering the overlapping community structure of complex networks in nature and society[J]. Nature, 2005, 435(7043):814-818. [16] LUO F, WANG J Z, PROMISLOW E. Exploring local community structures in large networks[C]//WI'06:Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence. Washington, DC:IEEE Computer Society, 2006:233-239. [17] LANCICHINETTI A, RADICCHI F, RAMASCO J J, et al. Finding statistically significant communities in networks[J]. PLoS One, 2011, 6(4):e18961. [18] GREGORY S. Finding overlapping communities in networks by label propagation[J]. New Journal of Physics, 2010, 12(10):103018. [19] XIE J R, SZYMANSKI B K. Towards linear time overlapping community detection in social networks[C]//Proceedings of the 2012 Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 7302. Berlin:Springer, 2012:25-36. [20] 项后军,裘斌斌,周宇.核心企业视角下不同集群演化过程的比较研究[J].科学学研究,2015,33(2):225-233.(XIANG H J, QIU B B, ZHOU Y. The comparative research of the evolution of different industrial clusters with the perspective of ieading firms[J]. Studies in Science of Science, 2015, 33(2):225-233.) [21] LANCICHINETTI A, FORTUNATO S, KERTÉSZ J. Detecting the overlapping and hierarchical community structure in complex networks[J]. New Journal of Physics, 2009, 11(3):033015. [22] LEE C, REID F, McDAID A, et al. Detecting highly overlapping community structure by greedy clique expansion[EB/OL].[2014-11-10]. https://arxiv.org/pdf/1002.1827.pdf. [23] ANDERSEN R, LANG K J. Communities from seed sets[C]//WWW'06:Proceedings of the 15th International Conference on World Wide Web. New York:ACM, 2006:223-232. [24] LANCICHINETTI A, FORTUNATO S. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities[J]. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2009, 80(1 Pt 2):016118. [25] 陈俊宇,周刚,南煜,等.一种半监督的局部扩展式重叠社区发现方法[J].计算机研究与发展,2016,53(6):1376-1388.(CHEN J Y, ZHOU G, NAN Y, et al. Semi-supervised local expansion method for overlapping community detection[J]. Journal of Computer Research and Development, 2016, 53(6):1376-1388.) [26] DANON L, DUCH J, DIAZ-GUILERA A, et al. Comparing community structure identification[J]. Journal of Statistical Mechanics:Theory and Experiment, 2005, 2005(9):P09008. |
[1] | 章悦, 张亮, 谢非, 杨嘉乐, 张瑞, 刘益剑. 基于实例分割模型优化的道路抛洒物检测算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3228-3233. |
[2] | 李凯, 李洁. 基于pinball损失的结构模糊多分类支持向量机算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3104-3112. |
[3] | 胡誉生, 何炳蔚, 邓清康. 混合视觉系统的运动物体检测和静态地图重建[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3332-3336. |
[4] | 高洁, 朱元, 陆科. 基于雷达和相机融合的目标检测方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3242-3250. |
[5] | 彭博, 罗娅茹, 谢盛华, 尹立雪. 联合深度学习的通用血流向量成像方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3368-3375. |
[6] | 陈吉成, 陈鸿昶. 基于张量建模和进化K均值聚类的社区检测方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3120-3126. |
[7] | 张嘉琪, 张月琴, 陈健. 优化强化学习路径特征分类的脉象识别法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3402-3408. |
[8] | 任俊伟, 曾诚, 肖丝雨, 乔金霞, 何鹏. 基于会话的多粒度图神经网络推荐模型[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3164-3170. |
[9] | 孙琳, 袁玉波. 基于人眼状态的瞌睡识别算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3213-3218. |
[10] | 葛晨宇, 董良, 许伊昆, 常毅, 张宏鸣. 基于总变分低秩组稀疏的全球雷达数据修复算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3353-3361. |
[11] | 闫钧华, 侯平, 张寅, 吕向阳, 马越, 王高飞. 基于多尺度多分类器卷积神经网络的混合失真类型判定方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3178-3184. |
[12] | 李福海, 蒋慕蓉, 杨磊, 谌俊毅. 基于生成对抗网络的梯度引导太阳斑点图像去模糊方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3345-3352. |
[13] | 曹建芳, 闫敏敏, 贾一鸣, 田晓东. 融合迁移学习的Inception-v3模型在古壁画朝代识别中的应用[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3219-3227. |
[14] | 刘太亨, 何昭水. 基于自编码和知识蒸馏的表面缺陷检测方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3200-3205. |
[15] | 张阳, 王小宁. 基于Word2Vec词嵌入和高维生物基因选择遗传算法的文本特征选择方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3151-3155. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||