Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Approximating the Advertisement Placement Problem
Journal of Scheduling
FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs
Proceedings of the 6th annual ACM international workshop on Web information and data management
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Detecting frauds in online advertising systems
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Data mining and audience intelligence for advertising
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Blog cascade affinity: analysis and prediction
Proceedings of the 18th ACM conference on Information and knowledge management
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
The state-of-the-art in personalized recommender systems for social networking
Artificial Intelligence Review
Affinity-driven blog cascade analysis and prediction
Data Mining and Knowledge Discovery
Hi-index | 0.00 |
Allowing global distribution of information to large audiences at very low cost, the Internet has emerged as a vital medium for marketing and advertising. Weblogs, a new form of self publication on the Internet, have attracted online advertisers because of their incredible growth-rate in recent years. In this paper, we propose to discover information diffusion paths from the blogosphere to track how information frequently flows from blog to blog. This knowledge can be used in various applications of online campaign. Our approach is based on analyzing the content of blogs. After detecting trackable topics of blogs, we model a blog community as a blog sequence database. Then, the discovery of information diffusion paths is formalized as a problem of frequent pattern mining. We develop a new data mining algorithm to discover information diffusion paths. Experiments conducted on real life dataset show that our algorithm discovers information diffusion paths efficiently. The discovered information diffusion paths are accurate in predicting the future information flow in the blog community.