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You searched for subject:(Epidemic cascade). Showing records 1 – 2 of 2 total matches.

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University of Texas – Austin

1. -0511-240X. Efficient approaches in network inference.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

Network based inference is almost ubiquitous in modern machine learning applications. In this dissertation we investigate several such problems motivated by applications in social networks, biological networks, recommendation system, targeted advertising etc. Unavailability of the graph, presence of latent factors, and large network size often make these inference tasks challenging. We develop both generative models and efficient algorithms to solve such problems. We provide analytical guarantees, in terms of accuracy and computation time, for all our algorithms and demonstrate their applicability on many real datasets. This dissertation mainly consists of two parts. In the first part we consider three different problems. We first consider the task of learning the Markov network structure in a discreet graphical model. We develop three fast greedy algorithms to solve this problem which succeeds even in graphs with strong non-neighbor interaction where previous convex optimization based methods fail. Next we consider the problem of learning latent user interests in different topics, using cascades which spread over a network. Our new algorithm infers both user interests and topics in large cascades, better than standard topic modeling algorithms which do not consider the network structure. In the third problem we develop a novel recursive algorithm based on convex relaxation to detect overlapping communities in a graph. The second part of the dissertation develops a mathematical framework to handle different sources of side information and use it to improve inference in networks. However first we demonstrate a much general technique to incorporate variety of side information in estimating a single component of a mixture model e.g. Gaussian mixture model, latent Dirichlet allocation, subspace clustering, and mixed linear regression. We then use a similar technique to solve the problem of identifying a single target community in a graph, using reference nodes or biased node weights as side information. Our algorithms are based on a variant of method of moments, and are much faster and more accurate than other unsupervised and semi-supervised algorithms. Advisors/Committee Members: Sanghavi, Sujay Rajendra, 1979- (advisor), Shakkottai, Sanjay (advisor), Baccelli, Francois (committee member), de Veciana, Gustavo (committee member), Caramanis, Constantine (committee member), Ravikumar, Pradeep (committee member).

Subjects/Keywords: Network inference; Graphical model; Epidemic cascade; Community detection; Mixture models; Side information; Semi-supervised

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-0511-240X. (2016). Efficient approaches in network inference. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46366

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-0511-240X. “Efficient approaches in network inference.” 2016. Thesis, University of Texas – Austin. Accessed March 20, 2019. http://hdl.handle.net/2152/46366.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-0511-240X. “Efficient approaches in network inference.” 2016. Web. 20 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0511-240X. Efficient approaches in network inference. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 20]. Available from: http://hdl.handle.net/2152/46366.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-0511-240X. Efficient approaches in network inference. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46366

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

2. Dey, Anindita. Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network.

Degree: MS, Computer Science, 2013, Arizona State University

In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.

Subjects/Keywords: Computer science; Epidemic Model; Information Cascade; Resource Constrained; Social Network

…two parts: Information Cascade and Epidemic Diffusion. 2.1 Information Cascades “ When… …82 viii LIST OF TABLES Table Page 3.1 Epidemic behavior models… …Varying Parameters : Epidemic Model used = SVS ,Network Topologies = [PA , smallworld… …distribution = uniform . Varying Parameters : Epidemic Model used = SIR, Network Topologies are PA… …uniform , resource = fixed , Threshold = fixed . Controlled Parameters : Epidemic Model used… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Dey, A. (2013). Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/20967

Chicago Manual of Style (16th Edition):

Dey, Anindita. “Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network.” 2013. Masters Thesis, Arizona State University. Accessed March 20, 2019. http://repository.asu.edu/items/20967.

MLA Handbook (7th Edition):

Dey, Anindita. “Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network.” 2013. Web. 20 Mar 2019.

Vancouver:

Dey A. Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network. [Internet] [Masters thesis]. Arizona State University; 2013. [cited 2019 Mar 20]. Available from: http://repository.asu.edu/items/20967.

Council of Science Editors:

Dey A. Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network. [Masters Thesis]. Arizona State University; 2013. Available from: http://repository.asu.edu/items/20967

.