Earticle

초록

영어

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in social networks. It is to find a small subset of nodes, which can spread the information or influence to the largest number of nodes. The conventional approaches consider information diffusion through the network in a coarse-grained manner, without taking into account the topical features of information content and users. However, for messages with different topics, the target influential spreaders may vary largely. In this paper, we propose to harness historical propagation data to learn the information diffusion probabilities on topic-level, based on which we use a greedy algorithm to iteratively select a set of influential nodes for a given topic. Specially, we design a three-stage algorithm named TopicRank to mine the most influential spreaders with respect to a specific topic. Given observed propagation data, we first use Latent Dirichlet Allocation (LDA) model to learn a topic mixture for each propagation message. Then, the topic-level diffusion probability of an edge is computed by exploiting the propagation actions occurred to it and the topic distribution of these propagation messages. Last, based on the learned topic-level diffusion probabilities, we apply optimized greedy algorithm CLEF to identify influential nodes with respect to a specific topic. Experimental results show that our method significantly outperforms state-of-the-art methods when used for topic-sensitive information spread maximization.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Computing topic-level Diffusion Probabilities
  3.1 Propagation data
  3.2. Topic Distillation
  3.3. Topic-level Diffusion Probability Computing
 4. Identifying Topic-Sensitive Influential Spreaders
  4.1. Topic-level diffusion graph
  4.2. Greedy Algorithm
 5. Experimental Evaluation
  5.1. Experiment Setup
  5.2. Experimental Results
 6. 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, Wuxi, China ]
• Wenbao Han [ State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, China, College of Cyber Space Security, Information Engineering University, Zhengzhou, China ]
• Yongjun Wang [ College of Computers, National University of Defense Technology, Changsha, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605