Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
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
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
Minimizing the spread of contamination by blocking links in a network
AAAI'08 Proceedings of the 23rd 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
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
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting information diffusion models over social networks for behavioral analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Detecting changes in opinion value distribution for voter model
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Learning diffusion probability based on node attributes in social networks
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Detecting anti-majority opinionists using value-weighted mixture voter model
DS'11 Proceedings of the 14th international conference on Discovery science
Behavioral analyses of information diffusion models by observed data of social network
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
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We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the patten boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.