Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
ACM SIGKDD Explorations Newsletter
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
A note on maximizing the spread of influence in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Detecting anti-majority opinionists using value-weighted mixture voter model
DS'11 Proceedings of the 14th international conference on Discovery science
| Hi-index | 0.00 |
Social networks play an important role for spreading information and forming opinions. A variety of voter models have been defined that help analyze how people make decisions based on their neighbors' decisions. In these studies, common practice has been to use the latest decisions in opinion formation process. However, people may decide their opinions by taking account not only of their neighbors' latest opinions, but also of their neighbors' past opinions. To incorporate this effect, we enhance the original voter model and define the temporal decay voter (TDV) model incorporating a temporary decay function with parameters, and propose an efficient method of learning these parameters from the observed opinion diffusion data. We further propose an efficient method of selecting the most appropriate decay function from among the candidate functions each with the optimized parameter values. We adopt three functions as the typical candidates: the exponential decay, the power-law decay, and no decay, and evaluate the proposed method (parameter learning and model selection) through extensive experiments. We, first, experimentally demonstrate, by using synthetic data, the effectiveness of the proposed method, and then we analyze the real opinion diffusion data from a Japanese word-of-mouth communication site for cosmetics using three decay functions above, and show that most opinions conform to the TDV model of the power-law decay function.