[1]赵冠哲,齐建鹏,于彦伟,等.移动社交网络异常签到在线检测算法[J].智能系统学报,2017,12(5):752-759.[doi:10.11992/tis.201706027]
ZHAO Guanzhe,QI Jianpeng,YU Yanwei,et al.Online check-in outlier detection method in mobile social networks[J].CAAI Transactions on Intelligent Systems,2017,12(5):752-759.[doi:10.11992/tis.201706027]
点击复制
ZHAO Guanzhe,QI Jianpeng,YU Yanwei,et al.Online check-in outlier detection method in mobile social networks[J].CAAI Transactions on Intelligent Systems,2017,12(5):752-759.[doi:10.11992/tis.201706027]
移动社交网络异常签到在线检测算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第5期
页码:
752-759
栏目:
学术论文—机器学习
出版日期:
2017-10-25
- Title:
- Online check-in outlier detection method in mobile social networks
- Keywords:
- location-based social networks; outlier detection; check-in location; distance-based outlier; friend circle; status of check-in; neighbor searching; time-triggered detection
- 分类号:
- TP391
- 摘要:
- 随着智能手机、Pad等智能移动设备的广泛普及，移动社交网络的应用得到了快速发展。本文针对移动社交网络中用户异常签到位置检测问题，提出了一类基于用户移动行为特征的异常签到在线检测方法。首先，在基于距离的异常模型基础上，提出了基于历史位置（H-Outlier）和基于好友圈（F-Outlier）两种异常签到模型；然后，针对H-Outlier提出了一种优化的检测算法H-Opt，利用所提的签到状态模型与优化的邻居搜索机制降低检测时间；针对F-Outlier提出了一种基于触发的优化检测算法F-Opt，将连续的在线异常检测转化成了基于触发的异常检测方式；最后，在真实的移动社交网络用户签到数据集上，验证了所提算法的有效性。实验结果显示，F-Opt显著降低了H-Opt的异常检测错误率；同时，相比于LUE算法，F-Opt和H-Opt的效率分别平均提升了2.34倍和2.45倍。
- Abstract:
- With the increasing popularization of smartphone, Pads and other smart mobile devices, the use of mobile social networks has also developed rapidly. In this paper, we propose an online method for detecting check-in outliers based on user mobility behavior in mobile social networks. First, based on a distance-based outlier model, we propose two check-in outlier models with respect to historical location (H-Outlier) and friend circle (F-Outlier), respectively. Second, for the H-Outlier, we propose an optimized detection algorithm called H-Opt, which utilizes the proposed check-in status model and an optimized neighbor searching mechanism to reduce computation time. For the F-Outlier, we propose a trigger-based optimized detection algorithm called F-Opt, which transforms continuous online outlier detection into trigger-based outlier detection. Lastly, we present our experimental results, based on a real-world check-in dataset, which demonstrate the effectiveness of the proposed algorithm. Our experimental results show that F-Opt significantly reduces the error rate of H-Opt outlier detection. In addition, compared with the LUE algorithm, the F-Opt and H-Opt algorithms improved efficiency by 2.34 and 2.45 times, respectively.
- 参考文献/References:
-
[1] 於志文, 周兴社, 郭斌. 移动社交网络中的感知计算模型、平台与实践[J]. 中国计算机学会通讯,2012,8(5):15-21.
[2] WE ARE SOCIAL LTD. DIGITAL IN 2016[EB/OL].[2017-03-10].http://wearesocial.com/uk/special-reports/digital-in-2016.
[3] ZHENG Yu, XIE X. Location-based social networks:locations[J]. Computing with spatial trajectories, 2011:277-308.
[4] 萧世埨. 基于位置服务与人类活动的关系和影响[J]. 中国计算机学会通讯, 2010,6(6):30-35.
[5] 张玉清, 吕少卿, 范丹. 在线社交网络中异常帐号检测方法研究[J]. 计算机学报, 2015, 38(10):2011-2027.ZHANG Yuqing,LV Shaoqing,FAN Dan. Anomaly detection in online social networks[J]. Chinese journal of computers, 2015, 38(10):2011-2027.
[6] CHO E, MYERS S A, LESKOVEC J. Friendship and mobility:user movement in location-based social networks[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego, USA, 2011:1082-1090.
[7] KNORR E M, NG R T. Algorithms for mining distance-based outliers in large datasets[C]//International Conference on Very Large Data Bases. New York, USA, 1998:392-403.
[8] KNORR E M, NG R T, TUCAKOV V. Distance-based outliers:algorithms and applications[J]. Vldb journal, 2000, 8(3/4):237-253.
[9] RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]//ACM SIGMOD International Conference on Management of Data. Dallas, USA, 2000:427-438.
[10] BREUNIG MARKUS M. LOF:identifying density-based local outliers[J]. ACM sigmod record, 2000, 29(2):93-104.
[11] YANG D, RUNDENSTEINER E A, Ward M O. Neighbor-based pattern detection for windows over streaming data[C]//International Conference on Extending Database Technology:Advances in Database Technology. Saint Petersburg, Russia, 2009:529-540.
[12] ANGIULLI F, FASSETTI F. Detecting distance-based outliers in streams of data[C]//Sixteenth ACM Conference on Conference on Information and Knowledge Management. Lisboa, Portugal, 2007:811-820.
[13] KONTAKI M, GOUNARIS A, PAPADOPOULOS A N, et al. Continuous monitoring of distance-based outliers over data streams[C]//IEEE, International Conference on Data Engineering. Hannover, Germany, 2011:135-146.
[14] CAO L, YANG D, WANG Q, et al. Scalable distance-based outlier detection over high-volume data streams[C]//International Conference on Data Engineering. Chicago,USA, 2014:76-87.
[15] LEE J G, HAN J, LI X. Trajectory outlier detection:a partition-and-detect framework[C]//International Confe-rence on Data Engineering. Cancun, Mexico, 2008:140-149.
[16] BU Yingyi, CHEN L, FU W C, et al. Efficient anomaly monitoring over moving object trajectory streams[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009:159-168.
[17] YU Yanwei, CAO Lei, RUNDENSTEINER E A, et al. Detecting moving object outliers in massive-scale trajectory streams[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014:422-431.
[18] LI Zhenhui, WANG J, Han J. Mining event periodicity from incomplete observations[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China, 2012:444-452.
- 相似文献/References:
-
[1]田国会,吉艳青,李晓磊.家庭智能空间下基于场景的人的行为理解[J].智能系统学报,2010,5(1):57.
TIAN Guo-hui,JI Yan-qing,LI Xiao-lei.Human behaviors understanding based on scene knowledge in home intelligent space[J].CAAI Transactions on Intelligent Systems,2010,5():57.
[2]张玉玲,尹传环.依特征频率的安卓恶意软件异常检测的研究[J].智能系统学报,2018,13(2):168.[doi:10.11992/tis.201609016]
ZHANG Yuling,YIN Chuanhuan.Android malware outlier detection based on feature frequency[J].CAAI Transactions on Intelligent Systems,2018,13():168.[doi:10.11992/tis.201609016]
[3]齐小刚,胡秋秋,刘立芳.基于MapReduce的并行异常检测算法[J].智能系统学报,2019,14(2):224.[doi:10.11992/tis.201809007]
QI Xiaogang,HU Qiuqiu,LIU Lifang.Parallel anomaly algorithm based on MapReduce[J].CAAI Transactions on Intelligent Systems,2019,14():224.[doi:10.11992/tis.201809007]
[4]王春凯,庄福振,史忠植.易变数据流的系统资源配置方法[J].智能系统学报,2019,14(6):1278.[doi:10.11992/tis.201908011]
WANG Chunkai,ZHUANG Fuzhen,SHI Zhongzhi.System resource allocation for variable data streams[J].CAAI Transactions on Intelligent Systems,2019,14():1278.[doi:10.11992/tis.201908011]
备注/Memo
收稿日期:2017-06-08。
基金项目:国家自然科学基金项目（61403328，61572419）；山东省重点研发计划项目（2015GSF115009）；山东省自然科学基金项目（ZR2014FQ016）；烟台大学研究生科技创新基金项目（YDZD1712）.
作者简介:赵冠哲,男,1992年生,硕士研究生,主要研究方向为数据挖掘;齐建鹏,男,1992年生,硕士研究生,主要研究方向为数据挖掘;于彦伟,男,1986年生,讲师,博士,主要研究方向为时空数据挖掘、流式数据处理、分布式计算。主持国家自然科学基金青年基金1项,参与国家自然科学基金面上项目1项,山东省重点研发计划项目1项。发表学术论文30余篇。
通讯作者:于彦伟.E-mail:yuyanwei@ytu.edu.cn
更新日期/Last Update:
2017-10-25