Inferring Networks of Diffusion and Influence. Gomez-Rodriguez, M., Leskovec, J., & Krause, A. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Proceeding, pages 1019--1028, June, 2010.
Inferring Networks of Diffusion and Influence [link]Paper  abstract   bibtex   
Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., 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 in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades 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 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.
@INPROCEEDINGS{Gomez-Rodriguez2010a,
  author = {Gomez-Rodriguez, Manuel and Leskovec, Jure and Krause, Andreas},
  title = {{Inferring Networks of Diffusion and Influence}},
  booktitle = {Proceedings of the 16th ACM SIGKDD International Conference on Knowledge
	Discovery and Data Mining},
  year = {2010},
  volume = {Proceeding},
  pages = {1019--1028},
  month = jun,
  abstract = {Information diffusion and virus propagation are fundamental processes
	talking place in networks. While it is often possible to directly
	observe when nodes become infected, observing individual transmissions
	(i.e., 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 in practice
	gives provably near-optimal performance. We demonstrate the effectiveness
	of our approach by tracing information cascades 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 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.},
  arxivid = {1006.0234},
  file = {:C$\backslash$:/Users/Dani/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gomez-Rodriguez, Leskovec, Krause - 2010 - Inferring Networks of Diffusion and Influence.pdf:pdf},
  keywords = {\#News\_Aggregator,\#attribution,\#content,\#disease\_outbreak,\#evolution,Data
	Structures and Algorithms,Machine Learning,Physics and Society},
  mendeley-tags = {\#News\_Aggregator,\#attribution,\#content,\#disease\_outbreak,\#evolution},
  url = {http://arxiv.org/abs/1006.0234}
}

Downloads: 0