[1]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]
Copy

Online check-in outlier detection method in mobile social networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 752-759 Column: 学术论文—机器学习 Public date: 2017-10-25
Title:
Online check-in outlier detection method in mobile social networks
Author(s):
ZHAO Guanzhe;  QI Jianpeng;  YU Yanwei;  LIU Zhaowei;  SONG Peng
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
Keywords:
location-based social networks; outlier detection; check-in location; distance-based outlier; friend circle; status of check-in; neighbor searching; time-triggered detection
CLC:
TP391
DOI:
10.11992/tis.201706027
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.
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.
Similar References:

Memo

-

Last Update: 2017-10-25

Copyright © CAAI Transactions on Intelligent Systems