Topic-aware Social Influence Propagation Models

被引:107
作者
Barbieri, Nicola [1 ]
Bonchi, Francesco [1 ]
Manco, Giuseppe [2 ]
机构
[1] Yahoo Res Barcelona, Barcelona, Spain
[2] CNR, ICAR, Arcavacata Di Rende, Italy
来源
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012) | 2012年
关键词
D O I
10.1109/ICDM.2012.122
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-world cascades than the standard propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. Next, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. We devise methods to learn the parameters of the models from a dataset of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.
引用
收藏
页码:81 / 90
页数:10
相关论文
共 23 条
  • [1] [Anonymous], 2010, WSDM
  • [2] [Anonymous], 1998, LEARNING GRAPHICAL M
  • [3] [Anonymous], 2003, 9 ACM SIGKDD INT C
  • [4] [Anonymous], 2007, KDD
  • [5] [Anonymous], KDD
  • [6] [Anonymous], KDD
  • [7] [Anonymous], PVLDB
  • [8] Latent Dirichlet allocation
    Blei, DM
    Ng, AY
    Jordan, MI
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) : 993 - 1022
  • [9] Bonchi F., 2011, IEEE INTELLIGENT INF, V12
  • [10] Chen W., 2010, KDD