The budgeted maximum coverage problem
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The Mathematics of Infectious Diseases
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Mining the network value of customers
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Learning Dynamic Bayesian Networks
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Learning probabilistic models of link structure
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Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Information diffusion through blogspace
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Structure and evolution of blogspace
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Graphs over time: densification laws, shrinking diameters and possible explanations
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Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Graph evolution: Densification and shrinking diameters
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Scalable modeling of real graphs using Kronecker multiplication
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Cost-effective outbreak detection in networks
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Statistical properties of community structure in large social and information networks
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Meme-tracking and the dynamics of the news cycle
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Learning graphical model structure using L1-regularization paths
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning influence probabilities in social networks
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Correcting for missing data in information cascades
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Supervised random walks: predicting and recommending links in social networks
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A framework for quantitative analysis of cascades on networks
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Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Peer and authority pressure in information-propagation models
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Who shall we follow in twitter for cyber vulnerability?
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Tracking and analyzing TV content on the web through social and ontological knowledge
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Detecting epidemics using highly noisy data
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
Solving the missing node problem using structure and attribute information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Picture tags and world knowledge: learning tag relations from visual semantic sources
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Social influence analysis and application on multimedia sharing websites
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Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Parameter learning for latent network diffusion
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Prediction in a microblog hybrid network using bonacich potential
Proceedings of the 7th ACM international conference on Web search and data mining
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Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 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.