Computer Science ›› 2018, Vol. 45 ›› Issue (6): 46-50.doi: 10.11896/j.issn.1002-137X.2018.06.008

• WISA2020 • Previous Articles     Next Articles

Community Discovery in Location Network

ZHENG Xiang-ping, YU Zhi-yong, WEN Guang-bin   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China;
    Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116,China
  • Received:2017-03-11 Online:2018-06-15 Published:2018-07-24

Abstract: The location network can portray the spatial structure of city from some unique perspectives.By studying the characteristics of urban location network and its difference with traditional social network,a community discovery algorithm based on location network was proposed.The algorithm takes into account the proximity of location,the connection between the locations and the similarity of user’s travel behavior.Firstly,the initial community is divided.Then,the extent of each site belonging to this community is interatively calculated the places with lower membership degree are adjusted until convergence,so as to find significant urban communities.The validity of the algorithm was verified by analyzing the attributes and correlations of the internal sites.

Key words: Community discovery, Crowd sensing, Location network

CLC Number: 

  • TP391
[1]QI J S,LIANG X,WANG Y.Overlapping community detection algorithm based on selection of seed nodes[J].Application Research of Computers,2017,34(12):3534-3537.(in Chinese)
齐金山,梁循,王怡.基于种子节点选择的重叠社区发现算法[J].计算机应用研究,2017,34(12):3534-3537.
[2]WU Z G,LU Z.A community discovery algorithm based on local similarity [J].Computer Engineering,2016,42(12):196-203.(in Chinese)
吴钟刚,吕钊.一种基于局部相似性的社区发现算法[J].计算机工程,2016,42(12):196-203.
[3]LIU Y,JI X S,LIU C X.Network community discovery optimization:based on random walk edge weight pretreatment method [J].Journal of Electronics & Information Technology,2013,35(10):2335-2340.(in Chinese)
刘阳,季新生,刘彩霞.网络社区发现优化:基于随机游走的边权预处理方法[J].电子与信息学报,2013,35(10):2335-2340.
[4]SONG C,ZHANG X K,FEI S,et al.Improved tag propagation algorithm based on random walk similarity matrix [J].Journal of Computer Applications and Software,2016,33(8):269-272.(in Chinese)
宋琛,张贤坤,费松,等.基于随机游走相似度矩阵的改进标签传播算法[J].计算机应用与软件,2016,33(8):269-272.
[5]HUANG F L,ZHANG S C,ZHU X F.Discovering Network Community Based on Multi-objective Optimization[J].Journal of Software,2013,24(9):2062-2077.(in Chinese)
黄发良,张师超,朱晓峰.基于多目标优化的网络社区发现方法[J].软件学报,2013,24(9):2062-2077.
[6]QIAO S J,GUO J,HAN N,et al.Large-scale complex network community parallel discovery algorithm [J].Journal of Compu-ter Science,2017,40(3):687-700.(in Chinese)
乔少杰,郭俊,韩楠,等.大规模复杂网络社区并行发现算法[J].计算机学报,2017,40(3):687-700.
[7]LIU H X,PENG S L.A method of community discovery based on neighboring nodes influencing intensity tag propagation [J].Modern Library and Information Technology,2015,31(4):58-64.(in Chinese)
刘郝霞,彭商濂.一种基于邻近节点影响强度标签传播社区发现方法[J].现代图书情报技术,2015,31(4):58-64.
[8]HAN L,ZHANG H.Community Discovery Method Based on Neighborhood Information [J].Pure Mathematics and Applied Mathematics,2015,31(1):85-92.(in Chinese)
韩路,张海.基于邻域信息的社区发现方法[J].纯粹数学与应用数学,2015,31(1):85-92.
[9]YAN F,ZHANG M,TAN Y W,et al.Community Discovery Method of Comprehensive Social Action Interest and Network Topology [J].Journal of Computer Research and Development,2010,47(z1):357-362.(in Chinese)
燕飞,张铭,谭裕韦,等.综合社会行动者兴趣和网络拓扑的社区发现方法[J].计算机研究与发展,2010,47(z1):357-362.
[10]YANG T B,JIN R,CHI Y,et al.Combining Link and content for community detection[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2009:927-936.
[11]KEWALRAMANI M N.Community detection in Twitter[D]. University of Maryland,2011.
[12]GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(12):7821-7826.
[13]NEWMAN M E,GIRVAN M.Finding and evaluating community structure in networks[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2004,69(2):026113.
[14]WILKINSON D M,HUBERMAN B A.A Method for Finding Communities of Related Genes[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl.1):5241-5248.
[15]PALLA G,VICSEK T.Quantifying social group evolution[J].Nature,2007,446(7136):664-667.
[16]RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical Review E,2007,76(2):036106:
[1] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[2] LI Xiao-dong, YU Zhi-yong, HUANG Fang-wan, ZHU Wei-ping, TU Chun-yu, ZHENG Wei-nan. Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring [J]. Computer Science, 2022, 49(5): 371-379.
[3] TANG Chun-yang, XIAO Yu-zhi, ZHAO Hai-xing, YE Zhong-lin, ZHANG Na. EWCC Community Discovery Algorithm for Two-Layer Network [J]. Computer Science, 2022, 49(4): 49-55.
[4] ZHANG Qing-qi, LIU Man-dan. Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery [J]. Computer Science, 2020, 47(8): 284-290.
[5] DONG Ming-gang, GONG Jia-ming and JING Chao. Multi-obJective Evolutionary Algorithm Based on Community Detection Spectral Clustering [J]. Computer Science, 2020, 47(6A): 461-466.
[6] LI Jian-Jun, WANG Xiao-ling, YANG Yu and FU Jia. Emergency Task Assignment Method Based on CQPSO Mobile Crowd Sensing [J]. Computer Science, 2020, 47(6A): 273-277.
[7] LIU Dan. Fog Computing and Self-assessment Based Clustering and Cooperative Perception for VANET [J]. Computer Science, 2020, 47(10): 55-62.
[8] LI Zhuo, XU Zhe, CHEN Xin, LI Shu-qin. Location-related Online Multi-task Assignment Algorithm for Mobile Crowd Sensing [J]. Computer Science, 2019, 46(6): 102-106.
[9] ZHOU Jie, YU Zhi-yong, GUO Wen-zhong, GUO Long-kun and ZHU Wei-ping. Participant Selection Algorithm for t-Sweep k-Coverage Crowd Sensing Tasks [J]. Computer Science, 2018, 45(2): 157-164.
[10] HE Xin, LIU Tian-xu, DING Shuang and BAI Lin. Optimization Selection Mechanism for Service Nodes in Hybrid Crowd Sensing [J]. Computer Science, 2017, 44(1): 113-116.
[11] PENG Li-zhen and WU Yang-yang. Semantic Similarity Computing Based on Community Mining of Wikipedia [J]. Computer Science, 2016, 43(4): 45-49.
[12] LI Yu-ling, WANG Xiu-ling and ZHOU Jian-ming. Cooperative Electromagnetic Compatibility Control Complexity Optimization Algorithm Based on Cluster Filter for Mobile Crowd Sensing [J]. Computer Science, 2016, 43(4): 115-117.
[13] LIU Jing-lian, WANG Da-ling, ZHAO Wei-ji, FENG Shi and ZHANG Yi-fei. Algorithm for Discovering Network Community with Centrality and Overlap [J]. Computer Science, 2016, 43(3): 33-37.
[14] LI Zhi-gang and TANG Xue-ming. User Incentive Mechanism Based on Crowd Search Optimization and Cooperative Competition for Mobile Crowd Sensing Networks [J]. Computer Science, 2016, 43(11): 184-189.
[15] CAO Xiao-chun, JING Li-hua, WANG Rui, ZHANG Rui, DONG Zhen-jiang and XIONG Hong-kai. Specific Content Monitoring on Social Networks Based on Social Computing and Deep Learning [J]. Computer Science, 2016, 43(10): 1-8.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!