Earticle

초록

영어

Information spread maximization is to find a small subset of nodes in social network such that they can maximize the expected spread of information. In this paper, we attempt harnessing historical information cascades data to learn how information propagates in social networks and how to maximize its spread. In particular, we proposed a voting algorithm to learn diffusion probabilities of edges from cascades data. Then a pruning method is developed to remove trivial edges whose weights are smaller than a threshold. Moreover, motivated by the social influence locality, we propose a Local Influence Model to evaluate node's influence within a local area instead of the whole network, which can effectively reduce the computational complexity. Based on Local Influence Model, we use greedy algorithm to find an approximate optimal solution. Experimental results show that our method significantly outperforms state-of-the-art models both in terms of information spread and algorithm runtime.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Information Spread Maximization Approach
  3.1. Problem Definition
  3.2. Learn Information Diffusion Probability of Edge
  3.3. Local Influence Computation
  3.4. Information Diffusion Maximization
 4. Experimental Evaluation
  4.1. Experiment Setup
  4.2. Experiment Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Donghao Zhou [ College of Computers, National University of Defense Technology, Changsha, China, State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China ]
• Wenbao Han [ State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China ]
• Yongjun Wang [ College of Computers, National University of Defense Technology, Changsha, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605