JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2018, Vol. 53 ›› Issue (3): 1-12.doi: 10.6040/j.issn.1671-9352.0.2017.371

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User influence analysis of social media with temporal characteristics

LIAO Xiang-wen1,2, ZHANG Ling-ying1,2, WEI Jing-jing3, GUI Lin1,2*, CHENG Xue-qi4, CHEN Guo-long1,2   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China;
    2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, Fujian, China;
    3. College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou 350108, Fujian, China;
    4. Key Laboratory of Network Data Science and Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2017-07-31 Online:2018-03-20 Published:2018-03-13

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Abstract: Since both the temporal characteristics and online learning are not fully considered in exsiting tensor influence models, a novel method with temporal characteristics is proposed in this paper. This method constructs tensor with users opinion, activity and network centrality information. Then, a factorizes tensor with stochastic gradient descent algorithm which is constrained by temporal characteristics matrix is deployed in our model. Base on these two steps above, this method calculates user influence by combining different slices of tensor in the end. The advantages of this method are that it can decompose tensor efficiently and satisfy the need of online learning. Experimental results show that the average accuracy of the proposed method is 2% to 6% better than the baseline method such as TwitterRank, OOLAM and constrained nonnegative tensor factorization method. Besides, the running time of the proposed method is only 30% to 50% of constrained nonnegative tensor factorization method.

Key words: Temporal Characteristics, Social Influence, Tensor

CLC Number: 

  • TP391
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