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커뮤니티 통계량에 기반한 사회 연결망 모니터링 절차
A social network monitoring procedure based on community statistics
Korean J Appl Stat 2023;36(5):399-413
Published online October 31, 2023
© 2023 The Korean Statistical Society.

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).
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
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