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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2020.9
- 수록면
- 197 - 202 (6page)
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초록· 키워드
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Online social networking platforms are used for the diffusion of information, ideas or news for various applications in our day to day life. Given a budget, the information diffusion to different entities of a network mainly depends on key entities that are initially selected as seed nodes for maximizing the influence within a network. Many studies have been done on Influence Maximization but they ignore the effect of community structure in finding influential seed nodes. This paper finds most influential seed nodes by finding smaller non-overlapping communities in a network and picking the best seed nodes in each community based on Degree centrality and Closeness centrality. This paper presents a comparative study of information diffusion through seed nodes selections by using different centrality measures and k-core decomposition method. We have used Linear Threshold (LT) diffusion Model for finding the number of activated nodes from a given set of seed nodes in one step. Our result shows that the proposed method is able to achieve maximum level of activation within a less budget as compared to five other measures. We have performed our analysis on four different datasets.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHODOLOGY
IV. ANALYSIS AND RESULT
V. CONCLUSION
REFERENCES
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