커뮤니티 통계량에 기반한 사회 연결망 모니터링 절차
A social network monitoring procedure based on community statistics
이주원a, 이재헌1,a
Joo Weon Leea, Jaeheon Lee1,a
a중앙대학교 응용통계학과
aDepartment of Applied Statistics, Chung-Ang University
1Department of Applied Statistics, Chung-Ang University, 84 Heukseok-Ro, Dongjak-Gu, Seoul 06974, Korea. E-mail: jaeheon@cau.ac.kr
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1A01050674).
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1A01050674).
Received March 23, 2023; Revised May 5, 2023; Accepted May 8, 2023.
- Abstract
- 최근 사회 연결망에서 비정상적인 변화를 모니터링하는 절차는 흥미로운 연구 주제이다. 이 논문은 사회 연결망 모형 중 커뮤니티와 개인들의 경향성을 모두 고려한 동적 연결망 모형인 DCSBM (degree corrected stochastic block model)을 가정하고 이 연결망 내의 변화를 모니터링하는 절차를 고려하였다. 이때 커뮤니티의 비정상적인 변화 탐지를 위해 세 가지의 모니터링 방법을 제안하였다. 또한 제안된 방법의 성능을 평가하기 위해 모의실험을 설계하고 수행하였다. 커뮤니티의 경향성 변화에 대한 모의실험 결과 연결망을 커뮤니티에 따라 분할하여 모니터링하는 방법이 전반적으로 빠르게 변화를 탐지하여 성능이 더 좋음을 알 수 있었다.
- Recently, monitoring and detecting anomalies in social networks have become an interesting research topic. In this study, we investigate the detection of abnormal changes in a network modeled by the DCSBM (degree corrected stochastic block model), which reflects the propensity of both individuals and communities. To this end, we propose three methods for anomaly detection in the DCSBM networks: One method for monitoring the entire network, and two methods for dividing and monitoring the network in consideration of communities. To compare these anomaly detection methods, we design and perform simulations. The simulation results show that the method for monitoring networks divided by communities has good performance.
- 주요어 : 비정상적인 변화 탐지, 사회 연결망, 연결망 모니터링, 통계적 공정 모니터링
- Keywords : abnormal detection, network monitoring, social network, statistical process monitoring
- References
- Abbe E, Bandeira AS, and Hall G (2016). Exact recovery in the stochastic block model, Information Theory IEEE Transactions on, 62, 471-487.
- Chau DH, Pandit S, and Faloutsos C (2006). Detecting fraudulent personalities in networks of online auctioneers, In Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery, Berlin, Germany, 103-114.
- Chin P, Rao A, and Vu V (2015). Stochastic block model and community detection in sparse graphs: A spectral algorithm with optimal rate of recovery, In Proceedings of The 28th Conference on Learning Theory, Paris, France, 391-423.
- Chung F and Lu L (2002). The average distances in random graphs with given expected degrees, Proceedings of the National Academy of Sciences, 99, 15879-15882.
- Erdos P and Renyi A (1959). On random graphs I, Publicationes Mathematicae, 6, 290-297.
- Everton SF and Cunningham D (2013). Detecting significant changes in dark networks, Behavioral Sciences of Terrorism and Political Aggression, 5, 94-114.
- Farahani EM, Kazemzadeh RB, Noorossana R, and Rahimian G (2017). A statistical approach to social network monitoring, Communications in Statistics-Theory and Methods, 46, 11272-11288.
- 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.
- Gao C, Ma Z, Zhang AY, and Zhou HH (2018). Community detection in degree-corrected block models, The Annals of Statistics, 46, 2153-2185.
- Holland PW, Laskey KB, and Leinhardt S (1983). Stochastic blockmodels: First steps, Social Networks, 5, 109-137.
- Hosseini SS and Noorossana R (2018). Performance evaluation of EWMA and CUSUM control charts to detect anomalies in social networks using average and standard deviation of degree measures, Quality and Reliability Engineering International, 34, 477-500.
- Karrer B and Newman ME (2011). Stochastic block models and community structure in networks, Physical Review E, 83, 016107-1 - 016107-10.
- Krebs VE (2002). Mapping networks of terrorist cells, Connections, 24, 43-52.
- Lee JW and Lee J (2021). Self-starting monitoring procedure for the dynamic degree corrected stochastic block model, The Korean Journal of Applied Statistics, 34, 25-38.
- Nowicki K and Snijders TA (2001). Estimation and prediction for stochastic blockstructures, Journal of the American Statistical Association, 96, 1077-1087.
- Priebe CE, Conroy JM, Marchette DJ, and Park Y (2005). Scan statistics on Enron graphs, Computational and Mathematical Organization Theory, 11, 229-247.
- Shetty J and Adibi J (2005). Discovering important nodes through graph entropy the case of Enron email database, In Proceeding of the 3rd International Workshop on Link Discovery, Chicago, USA, 74-81.
- Snijders TA and Nowicki K (1997). Estimation and prediction for stochastic blockmodels for graphs with latent block structure, Journal of Classification, 14, 75-100.
- Vargas JA (2012). Spring awakening: How an Egyptian revolution began on facebook, The New York Times, Sunday Book Review,
- Wilson JD, Stevens NT, and Woodall WH (2019). Methods for monitoring multiple proportions when inspecting continuously, Journal of Quality Technology, 43, 237-248.
- Xu KS and Hero AO (2013). Dynamic stochastic block models: Statistical models for time evolving networks, In Social Computing, Behavioral-Cultural Modeling and Prediction, Washington DC, USA, 201-210.
- Yu L, Woodall WH, and Tsui KL (2018). Detecting node propensity changes in the dynamic degree corrected stochastic block model, Social Networks, 54, 209-227.
- Yu L, Zwetsloot IM, Stevens NT, Wilson JD, and Tsui KL (2022). Monitoring dynamic networks: A simulation-based strategy for comparing monitoring methods and a comparative study, Quality and Reliability Engineering International, 38, 1226-1250.
- Zhang AY and Zhou HH (2016). Minimax rates of community detection in stochastic block models, The Annals of Statistics, 44, 2252-2280.
Full Text(PDF) Free