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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

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).

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