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ABSTRACT: One of the interesting and important problems of information diffusion over a
large social network is to identify an appropriate model from a limited amount
of diffusion information. There are two contrasting approaches to model
information diffusion: a push type model known as Independent Cascade (IC)
model and a pull type model known as Linear Threshold (LT) model. We extend
these two models (called AsIC and AsLT in this paper) to incorporate
asynchronous time delay and investigate 1) how they differ from or similar to
each other in terms of information diffusion, 2) whether the model itself is
learnable or not from the observed information diffusion data, and 3) which
model is more appropriate to explain for a particular topic (information) to
diffuse/propagate. We first show there can be variations with respect to how
the time delay is modeled, and derive the likelihood of the observed data being
generated for each model. Using one particular time delay model, we show the
model parameters are learnable from a limited amount of observation. We then
propose a method based on predictive accuracy by which to select a model which
better explains the observed data. Extensive evaluations were performed. We
first show using synthetic data with the network structures taken from real
networks that there are considerable behavioral differences between the AsIC
and the AsLT models, the proposed methods accurately and stably learn the model
parameters, and identify the correct diffusion model from a limited amount of
observation data. We next apply these methods to behavioral analysis of topic
propagation using the real blog propagation data, and show there is a clear
indication as to which topic better follows which model although the results
are rather insensitive to the model selected at the level of discussing how far
and fast each topic propagates from the learned parameter values.
04/2012;
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Knowl. Inf. Syst. 01/2012; 30:613-635.
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ABSTRACT: We addressed the problem of detecting the change in behavior of information
diffusion from a small amount of observation data, where the behavior changes
were assumed to be effectively reflected in changes in the diffusion parameter
value. The problem is to detect 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
diffusion sequences. The naive learning algorithm has to iteratively update the
patten boundaries, each requiring optimization of diffusion parameters by the
EM algorithm, and is very inefficient. We devised a very efficient search
algorithm using the derivative of likelihood which avoids parameter value
optimization during the search. The results tested using three real world
network structures confirmed that the algorithm can efficiently identify the
correct change pattern. We further compared our algorithm with the naive method
that finds the best combination of change boundaries by an exhaustive search
through a set of randomly selected boundary candidates, and showed that the
proposed algorithm far outperforms the native method both in terms of accuracy
and computation time.
10/2011;
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Journal of Machine Learning Research - Proceedings Track. 01/2011; 20:263-280.
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Journal of Machine Learning Research - Proceedings Track. 01/2011; 20:297-313.
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Intell. Data Anal. 01/2011; 15:633-652.
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Foundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Warsaw, Poland, June 28-30, 2011. Proceedings; 01/2011
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Discovery Science - 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings; 01/2011
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Social Computing, Behavioral-Cultural Modeling and Prediction - 4th International Conference, SBP 2011, College Park, MD, USA, March 29-31, 2011. Proceedings; 01/2011
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AI 2011: Advances in Artificial Intelligence - 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings; 01/2011
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Data Min. Knowl. Discov. 01/2010; 20:70-97.
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Knowledge Management and Acquisition for Smart Systems and Services, 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010. Proceedings; 01/2010
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Discovery Science - 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010. Proceedings; 01/2010
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Knowledge Management and Acquisition for Smart Systems and Services, 11th International Workshop, PKAW 2010, Daegu, Korea, August 20 - September 3, 2010. Proceedings; 01/2010
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Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010; 01/2010
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Journal of Machine Learning Research - Proceedings Track. 01/2010; 13:193-208.
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Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III; 01/2010
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PRICAI 2010: Trends in Artificial Intelligence, 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30-September 2, 2010. Proceedings; 01/2010
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Advances in Social Computing, Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, Bethesda, MD, USA, March 30-31, 2010. Proceedings; 01/2010
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ABSTRACT: We address the problem of ranking influential nodes in complex social networks by estimating diffusion probabilities from
observed information diffusion data using the popular independent cascade (IC) model. For this purpose we formulate the likelihood
for information diffusion data which is a set of time sequence data of active nodes and propose an iterative method to search
for the probabilities that maximizes this likelihood. We apply this to two real world social networks in the simplest setting
where the probability is uniform for all the links, and show that the accuracy of the probability is outstandingly good, and
further show that the proposed method can predict the high ranked influential nodes much more accurately than the well studied
conventional four heuristic methods.
04/2009: pages 1-8;
