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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


This thesis studies recommendation systems and considers joint sampling and learning. Sampling in recommendation systems is to obtain users' ratings on specific items chosen by the recommendation platform, and learning is to infer the unknown ratings of users to items given the existing data. In this thesis, the problem is formulated as an adaptive matrix completion problem in which sampling is to reveal the unknown entries of a $U\times M$ matrix where $U$ is the number of users, $M$ is the number of items, and each entry of the $U\times M$ matrix represents the rating of a user to an …

Contributors
Zhu, Lingfang, Xue, Guoliang, He, Jingrui, et al.
Created Date
2015

The National Basketball Association (NBA) is the most popular basketball league in the world. The world-wide mighty high popularity to the league leads to large amount of interesting and challenging research problems. Among them, predicting the outcome of an upcoming NBA match between two specific teams according to their historical data is especially attractive. With rapid development of machine learning techniques, it opens the door to examine the correlation between statistical data and outcome of matches. However, existing methods typically make predictions before game starts. In-game prediction, or real-time prediction, has not yet been sufficiently studied. During a match, data …

Contributors
Lin, Rongyu, Tong, Hanghang, He, Jingrui, et al.
Created Date
2017

Network mining has been attracting a lot of research attention because of the prevalence of networks. As the world is becoming increasingly connected and correlated, networks arising from inter-dependent application domains are often collected from different sources, forming the so-called multi-sourced networks. Examples of such multi-sourced networks include critical infrastructure networks, multi-platform social networks, cross-domain collaboration networks, and many more. Compared with single-sourced network, multi-sourced networks bear more complex structures and therefore could potentially contain more valuable information. This thesis proposes a multi-layered HITS (Hyperlink-Induced Topic Search) algorithm to perform the ranking task on multi-sourced networks. Specifically, each node in …

Contributors
Yu, Haichao, Tong, Hanghang, He, Jingrui, et al.
Created Date
2018

The game held by National Basketball Association (NBA) is the most popular basketball event on earth. Each year, tons of statistical data are generated from this industry. Meanwhile, managing teams, sports media, and scientists are digging deep into the data ocean. Recent research literature is reviewed with respect to whether NBA teams could be analyzed as connected networks. However, it becomes very time-consuming, if not impossible, for human labor to capture every detail of game events on court of large amount. In this study, an alternative method is proposed to parse public resources from NBA related websites to build degenerated …

Contributors
Zhang, Xiaoyu, Tong, Hanghang, He, Jingrui, et al.
Created Date
2017

Cyberbullying is a phenomenon which negatively affects individuals. Victims of the cyberbullying suffer from a range of mental issues, ranging from depression to low self-esteem. Due to the advent of the social media platforms, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include use of standards and guidelines, human moderators, use of blacklists based on profane words, and regular expressions to manually detect cyberbullying. However, these mechanisms fall short in social media and do not scale well. Users in social media use intentional evasive expressions like, obfuscation of abusive words, which necessitates the development of …

Contributors
Dani, Harsh, Liu, Huan, Tong, Hanghang, et al.
Created Date
2017

Measuring node centrality is a critical common denominator behind many important graph mining tasks. While the existing literature offers a wealth of different node centrality measures, it remains a daunting task on how to intervene the node centrality in a desired way. In this thesis, we study the problem of minimizing the centrality of one or more target nodes by edge operation. The heart of the proposed method is an accurate and efficient algorithm to estimate the impact of edge deletion on the spectrum of the underlying network, based on the observation that the edge deletion is essentially a local, …

Contributors
Peng, Ruiyue, Tong, Hanghang, He, Jingrui, et al.
Created Date
2016