KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Estimating the Rumor Source by Rumor Centrality Based Query in Networks

네트워크에서 루머 중심성 기반 질의를 통한 루머의 근원 추정

  • 최재영 (호남대학교 미래자동차공학부)
  • Received : 2018.12.11
  • Accepted : 2019.03.01
  • Published : 2019.07.31
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Abstract

In this paper, we consider a rumor source inference problem when sufficiently many nodes heard the rumor in the network. This is an important problem because information spread in networks is fast in many real-world phenomena such as diffusion of a new technology, computer virus/spam infection in the internet, and tweeting and retweeting of popular topics and some of this information is harmful to other nodes. This problem has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees if the number of infected nodes is sufficiently large. Motivated by this, we study the impact of query that is asking some additional question to the candidate nodes of the source and propose budget assignment algorithms of a query when the network administrator has a finite budget. We perform various simulations for the proposed method and obtain the detection probability that outperforms to the existing prior works.

본 논문에서는 네트워크에서 충분히 많은 노드가 루머를 들었을 때 그 근원이 어디서부터 시작 되었는지를 추론하는 문제를 고려한다. 이것은 신기술의 확산, 인터넷에서의 컴퓨터 바이러스/스팸 감염, 인기 있는 주제의 tweeting 및 retweeting과 같은 많은 실제 환경에서 네트워크의 정보 확산이 빠르게 진행되고, 이 정보 중 일부는 다른 노드에게 악영향을 미칠 수 있기 때문에 매우 중요한 문제이다. 이 문제는 선행연구에 의해 감염된 노드의 수가 충분히 많으면 정규 트리의 경우에도 탐지 확률이 31%를 초과 할 수 없다는 것이 입증되었다. 이를 바탕으로 네트워크에 감염된 후보 노드에게 몇 가지 추가 질의를 하는 방법에 대해 조사하고 네트워크 관리자가 한정된 자산을 가지고 있을 때 각 노드에 대한 질의의 수를 어떻게 분배하는지에 대한 자산 할당 알고리즘을 제안한다. 마지막으로 제안한 방법에 대하여 다양한 시뮬레이션을 수행하였고 기존 선행 연구보다 우수한 성능을 확인하였다.

Keywords

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Fig. 1. Rumor Spreading on Social Networks

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Fig. 2. Identity/Direction Query

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Fig. 3. Batch Query and Interactive Query with Untruthful Answers[3]

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Fig. 4. Proposed Budget Assignment Method

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Fig. 5. Interactive Query from the Rumor Center

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Fig. 6. Facebook Network[2]

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Fig. 7. Detection Probability for Batch Query on Regular Tree(degree=3)

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Fig. 8. Detection Probability for Interactive Query on Regular Tree (degree=3)

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Fig. 9. Detection Probability for Batch Query on ER Random Graph

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Fig. 10. Detection Probability for Interactive Query on ER Random Graph

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Fig. 11. Detection Probability for Batch Query on Facebook Graph

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Fig. 12. Detection Probability for Interactive Query on Facebook Graph

Table 1. Taxonomy of Rumor Source Detection Problems

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Table 2. Detection Probabilities for Batch Query using Various (p,q)(K=500)(H: Homogeneous, P: Proposed)

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Table 3. Detection Probabilities for Interactive Query using Various (p,q)(K=500)(H: Homogeneous, P: Proposed)

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