Structure and dynamics of diffusion networks

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Diffusion of information, ideas, behaviors and diseases are ubiquitous in nature and modern society. One of the main goals of this dissertation is to shed light on the hidden underlying structure of diffusion. To this aim, we developed flexible probabilistic models and inference algorithms that make minimal assumptions about the physical, biological or cognitive mechanisms responsible for diffusion. We avoid modeling the mechanisms underlying individual activations, and instead develop a data-driven approach which uses only the visible temporal traces diffusion generates. We first developed two algorithms, NetInf and MultiTree, that infer the network structure or skeleton over which diffusion takes place. However, both algorithms assume networks to be static and diffusion to occur at equal rates across different edges. We then developed NetRate, an algorithm that allows for static and dynamic networks with different rates across different edges. NetRate infers not only the network structure but also the rate of every edge. Finally, we develop a general theoretical framework of diffusion based on survival theory. Our models and algorithms provide computational lenses for understanding the structure and temporal dynamics that govern diffusion and may help towards forecasting, influencing and retarding diffusion, broadly construed. As an application, we study information propagation in the online media space. We find that the information network of media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan's civil war or Syria's uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites. Additionally, we apply our probabilistic framework of diffusion to the influence maximization problem and develop the algorithm MaxInf. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by considering the temporal dynamics of diffusion.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2013
Issuance monographic
Language English


Associated with Gomez Rodriguez, Manuel
Associated with Stanford University, Department of Electrical Engineering.
Primary advisor Ng, Andrew Y, 1976-
Thesis advisor Ng, Andrew Y, 1976-
Thesis advisor Candès, Emmanuel J. (Emmanuel Jean)
Thesis advisor Leskovec, Jurij
Thesis advisor Schölkopf, Bernhard
Advisor Candès, Emmanuel J. (Emmanuel Jean)
Advisor Leskovec, Jurij
Advisor Schölkopf, Bernhard


Genre Theses

Bibliographic information

Statement of responsibility Manuel Gomez Rodriguez.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2013.
Location electronic resource

Access conditions

© 2013 by Manuel Gomez Rodriguez
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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