| dc.identifier.uri | http://hdl.handle.net/11401/77305 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | As the complexity of online activities increases, social network structures have come to play an increasingly important role in the experience and effectiveness of an individual's online life. These structures exhibit a high degree of dynamism. Also, online platforms have provided us with unprecedented opportunities to study behavior and dynamics of these network structures. Understanding whether/why/when a person will behave in a certain manner can be important in a number of social domains. In our work, we model these network dynamics and design accurate prediction algorithms for these behavioral models. The main challenges that we address in this dissertation are 1) the ability to predict imminent departure events and the probable adverse impact of these events, 2) understanding the processes that drive group growth and stability, and 3) implications of social influence on opinion and relationship formation. Our work offers interesting insights on factors that cause and affect these network events. The methods proposed in our study use a diverse set of features that help us in building richer predictive models that result in more accurate predictions. Another aspect of our research deals with the innovative use of spectral graph theory concepts in unifying activity information of people across different social platforms. We also contribute in the development of a large-scale news and blog analysis engine that provides ready access to a wealth of interesting statistics on millions of people, places, and things across a number of interesting web corpora. The work we present has a wide range of applications: helping spot malicious behavior, forecasting group stability, predicting churn, recommending better content, deanonymizing network identities, and detecting trends in news data. Our techniques have been evaluated and validated on several large-scale, real-world datasets that span different domains. | |
| dcterms.available | 2017-09-20T16:52:25Z | |
| dcterms.contributor | Gao, Jie | en_US |
| dcterms.contributor | Skiena, Steven | en_US |
| dcterms.contributor | van de Rijt, Arnout | en_US |
| dcterms.contributor | Liu, Juan. | en_US |
| dcterms.creator | Patil, Akshay | |
| dcterms.dateAccepted | 2017-09-20T16:52:25Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:25Z | |
| dcterms.description | Department of Computer Science. | en_US |
| dcterms.extent | 177 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77305 | |
| dcterms.issued | 2015-08-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:25Z (GMT). No. of bitstreams: 1
Patil_grad.sunysb_0771E_11453.pdf: 9752637 bytes, checksum: 247f0c18bd4dbbaae44594acd36db129 (MD5)
Previous issue date: 2013 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Computer science | |
| dcterms.title | Analyzing Dynamics in Online Social Networks | |
| dcterms.type | Dissertation | |
