Modeling information flow in networks : competition, evolution, and external influence
Abstract/Contents
- Abstract
- In online social networks such as Twitter and Facebook, users are constantly sharing information with people they are connected to, as well as re-sharing information posted by others. Through this process, a single piece of information called a contagion can spread from user to user over the connections until it has reached a large portion of the network. In this thesis, we develop a series of probabilistic methods for modeling the spread of contagions in social networks in order to better understand the factors that affect the process. Our work examines several different phenomena that affect information flows through social networks. One such phenomenon is unobserved sources of information influencing members of the network. We present a model that not only quantifies these hidden information sources but also provides a more accurate view of information spread. We find that as much as 29% of all information spreading through social networks like Twitter originates from sources outside the network. Next, we examine how different contagions spreading through a network can interact with each other. We observe and model competition (when one contagion can decrease the spread of another contagion) and cooperation (when one contagion increases the spread of another). We find that contagion interaction can increase or decrease the probability of contagion spread by more than 70% on average. We also explore the dynamic nature of social network structure, and how these dynamics are affected by the spread of information. As social network users are exposed to new contagions, they are constantly forming new connections with other users as well as deleting connections. We find that the spread of contagions can cause sudden "bursts" in both the creation of new connections and the deletion of old connections. We also find that contagions can change the network structure on a global scale by moving like-minded members closer to each other as well as pushing less similar users farther away. Additionally, we consider the problem of inferring the structure of a hidden network when only patterns of information spread are known. Given only the timing of when each user adopted each piece of information, our method accurately predicts which users are connected to which other users by converting the maximum likelihood estimation of the network into a series of independent convex optimization problems that can be solved efficiently. Taken together, the results presented in this thesis make contributions to understanding how information flows through networks, and they provide insights into the nature of how people exchange information.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Myers, Seth A |
|---|---|
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
| Primary advisor | Leskovec, Jurij |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Johari, Ramesh, 1976- |
| Advisor | Hastie, Trevor |
| Advisor | Johari, Ramesh, 1976- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Seth A. Myers. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Seth Alexander Myers
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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