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Structure and Dynamics of Information Pathways in Online Media
TLDR
An on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem and studies the evolution of information pathways in the online media space.Abstract:
Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network.
We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. 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.read more
Citations
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Journal ArticleDOI
Information diffusion in online social networks: a survey
TL;DR: A survey of representative methods dealing with information diffusion in social networks and a taxonomy that summarizes the state-of-the-art is proposed, intended to help researchers in quickly understanding existing works and possible improvements to bring.
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SNAP: A General-Purpose Network Analysis and Graph-Mining Library
Jure Leskovec,Rok Sosic +1 more
TL;DR: The Stanford Network Analysis Platform (SNAP) as mentioned in this paper is a general-purpose, high-performance system that provides easy-to-use, highlevel operations for analysis and manipulation of large networks.
Journal ArticleDOI
Dynamics of information diffusion and its applications on complex networks
Zi-Ke Zhang,Zi-Ke Zhang,Chuang Liu,Xiu-Xiu Zhan,Xiu-Xiu Zhan,Xin Lu,Xin Lu,Chuxu Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang +9 more
TL;DR: It is emphasized that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.
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Measuring Emotional Contagion in Social Media
TL;DR: The dynamics of emotional contagion is studied using a random sample of Twitter users, whose activity was observed during a week of September 2014, and the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce is highlighted.
Journal ArticleDOI
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
TL;DR: Graph signal processing (GSP) has been widely used to infer the underlying graph topology as discussed by the authors, where correlation analysis takes center stage along with its connections to covariance selection and high dimensional regression for learning Gaussian graphical models.
References
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