Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Modern Information Retrieval
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
ACM Transactions on Internet Technology (TOIT)
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
PageRank: standing on the shoulders of giants
Communications of the ACM
Identifying relevant social media content: leveraging information diversity and user cognition
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Influence and passivity in social media
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Multiplying matrices faster than coppersmith-winograd
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community. In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations. Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.