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
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Learning diffusion probability based on node attributes in social networks
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Active learning of model parameters for influence maximization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Graph embedding on spheres and its application to visualization of information diffusion data
Proceedings of the 21st international conference companion on World Wide Web
A predictive model for the temporal dynamics of information diffusion in online social networks
Proceedings of the 21st international conference companion on World Wide Web
Detecting changes in information diffusion patterns over social networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Don't count the number of friends when you are spreading information in social networks
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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We investigate how well different information diffusion models can explain observation data by learning their parameters and discuss which model is better suited to which topic. We use two models (AsIC, AsLT), each of which is an extension of the well known Independent Cascade (IC) and Linear Threshold (LT) models and incorporates asynchronous time delay. The model parameters are learned by maximizing the likelihood of observation, and the model selection is performed by choosing the one with better predictive accuracy. We first show by using four real networks that the proposed learning algorithm correctly learns the model parameters both accurately and stably, and the proposed selection method identifies the correct diffusion model from which the data are generated. We next apply these methods to behavioral analysis of topic propagation using the real blog propagation data, and show that although the relative propagation speed of topics that are derived from the learned parameter values is rather insensitive to the model selected, there is a clear indication as to which topic better follows which model. The correspondence between the topic and the model selected is well interpretable.