응용통계연구 (The Korean Journal of Applied Statistics)

  • 제35권2호
  • /
  • Pages.299-310
  • /
  • 2022
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

분할된 네트워크에 기반한 사회 네트워크 모니터링 절차

Social network monitoring procedure based on partitioned networks

  • 홍휘주 (중앙대학교 응용통계학과) ;
  • 이주원 (중앙대학교 응용통계학과) ;
  • 이재헌 (중앙대학교 응용통계학과)
  • Hong, Hwiju (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Joo Weon (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Jaeheon (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2021.12.23
  • 심사 : 2022.01.20
  • 발행 : 2022.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

사회 네트워크 분석에 대한 관심이 높아짐에 따라 사회 네트워크에서 발생하는 변화를 탐지하는 연구에 대한 관심도 높아지고 있다. 사회 네트워크에서 발생하는 변화는 네트워크의 구조적 변화로 나타난다. 따라서 사회 네트워크에서 발생하는 변화를 탐지하는 것은 네트워크의 구조적 특성에 대한 변화를 탐지하는 것이다. 사회 네트워크에서 발생하는 지역적 변화는 가까운 이웃들 간에 발생하는 변화로 네트워크 일부에 집단적으로 나타난다. 이 논문의 목적은 네트워크에서 발생하는 지역적 변화를 효율적으로 탐지하는 절차를 제안하는 것이다. 제안하는 절차는 지역적 변화를 보다 효율적으로 탐지하기 위해 네트워크를 분할하고 각각의 분할된 네트워크에 기반한 관리도를 작성하여 네트워크에서 발생한 변화를 탐지하는 것이다. 네트워크를 분할하여 변화를 탐지하는 절차는 네트워크에서 발생한 지역적 변화를 보다 신속하게 탐지할 수 있으며, 변화가 발생한 위치에 대한 정보를 제공한다는 장점이 있다. 모의실험 결과에 따르면 제안된 절차는 네트워크의 크기가 작고 변화의 크기가 작은 경우 효율적이며, 네트워크를 더 작은 크기로 분할하면 작은 변화를 더 효율적으로 탐지한다는 사실을 확인하였다.

As interest in social network analysis increases, researchers' interest in detecting changes in social networks is also increasing. Changes in social networks appear as structural changes in the network. Therefore, detecting a change in a social network is detecting a change in the structural characteristics of the network. A local change in a social network is a change that occurs in a part of the network. It usually occurs between close neighbors. The purpose of this paper is to propose a procedure to efficiently detect local changes occurring in the network. In this paper, we divide the network into partitioned networks and monitor each partitioned network to detect local changes more efficiently. By monitoring partitioned networks, we can detect local changes more quickly and obtain information about where the changes are occurring. Simulation studies show that the proposed method is efficient when the network size is small and the amount of change is small. In addition, under a fixed overall false alarm rate, when we partition the network into smaller sizes and monitor smaller partitioned networks, it detects local changes better.

키워드

과제정보

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2020R1F1A1A01050674).

참고문헌

  1. Akoglu L, Chandy R, and Faloutsos C (2013). Opinion fraud detection in online reviews by network effects. In Proceedings of the International AAAI Conference on Web and Social Media, 7.
  2. Chau DH, Pandit S, and Faloutsos C (2006). Detecting fraudulent personalities in networks of online auctioneers. In European conference on principles of data mining and knowledge discovery, Springer, 103-114.
  3. Cheng A and Dickinson P (2013). Using scan-statistical correlations for network change analysis. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 1-13.
  4. Chung F and Lu L (2002). The average distances in random graphs with given expected degrees. In Proceedings of the National Academy of Sciences, 99, 15879-15882. https://doi.org/10.1073/pnas.252631999
  5. Erdos P and Renyi A (1959). On random graphs I, Publicationes Mathematicae Debrecen, 6, 290-297. https://doi.org/10.5486/PMD.1959.6.3-4.12
  6. Fire M, Katz G, and Elovici Y (2012). Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies, Human Journal, 1, 26-39.
  7. Holland PW, Laskey KB, and Leinhardt S (1983). Stochastic blockmodels: first steps, Social Networks, 5, 109-137. https://doi.org/10.1016/0378-8733(83)90021-7
  8. Karrer B and Newman ME (2011). Stochastic blockmodels and community structure in network, Physical Review E, 83, 016107. https://doi.org/10.1103/physreve.83.016107
  9. Krebs VE (2002). Mapping networks of terrorist cells, Connections, 24, 43-52.
  10. Malm A and Bichler G (2011). Networks of collaborating criminals: Assessing the structural vulnerability of drug markets, Journal of Research in Crime and Delinquency, 48, 271-297. https://doi.org/10.1177/0022427810391535
  11. McCulloh I and Carley KM (2011). Detecting change in longitudinal social networks, Military Academy West Point NY Network Science Center (NSC).
  12. Nowicki K and Snijders TA (2001). Estimation and prediction for stochastic block structures, Journal of the American Statistical Association, 96, 1077-1087. https://doi.org/10.1198/016214501753208735
  13. Page ES (1954). Continuous inspection schemes, Biometrika, 41, 100-115. https://doi.org/10.1093/biomet/41.1-2.100
  14. Pandit S, Chau DH, Wang S, and Faloutsos C (2007). Netprobe: a fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th International Conference on World Wide Web, 201-210.
  15. Phua C, Lee V, Smith K, and Gayler R (2010). A Comprehensive Survey of Data Mining-Based Fraud Detection Research, arXiv preprint arXiv :1009.6119
  16. Quesenberry CP (1991a). SPC Q charts for a binomial parameter p: short or long runs, Journal of quality technology, 23, 239-246. https://doi.org/10.1080/00224065.1991.11979329
  17. Quesenberry CP (1991b). SPC Q charts for a Poisson parameter λ: short or long runs, Journal of Quality Technology, 23, 296-303. https://doi.org/10.1080/00224065.1991.11979345
  18. Roberts SW (1959). Control chart tests based on geometric moving averages, Technometrics, 42, 97-101. https://doi.org/10.1080/00401706.2000.10485986
  19. Savage D, Zhang X, Yu X, Chou P, and Wang Q (2014). Performance evaluation of social network anomaly detection using a moving anomaly detection in online social networks, Social networks, 39, 62-70. https://doi.org/10.1016/j.socnet.2014.05.002
  20. Shetty J and Adibi J (2005). Discovering important nodes through graph entropy the case of enron email database. In Proceedings of the 3rd International Workshop on Link Discovery, 23, 74-81.
  21. Shewhart WA (1931). Economic Control of Quality of Manufactured Product, Van Nostrand, New York.
  22. Snijders TA and Nowicki K (1997). Estimation and prediction for stochastic block models for graphs with latent block structure, Journal of Classification, 14, 75-100. https://doi.org/10.1007/s003579900004
  23. Zhao MJ, Driscoll AR, Sengupta S, Fricker Jr RD, Spitzner DJ, and Woodall WH (2018). Performance evaluation of social network anomaly detection using a moving window-based scan method, Quality and Reliability Engineering International, 34, 1699-1716. https://doi.org/10.1002/qre.2364