Weighting Links Using Lexical and Positional Analysis in Web Ranking
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Inferring useful heuristics from the dynamics of iterative relational classifiers
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semi-supervised Classification and Noise Detection
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Empirical comparison of "hard" and "soft" label propagation for relational classification
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate and scalable nearest neighbors in large networks based on effective importance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Classification on networked data plays an important role in many problems such as web page categorization, classification of bibliographic information network, etc... Most classification algorithms on information networks work by iteratively propagating information through network graphs. One important issue concerning iterative classifiers is that false inferences made at some point in iteration might propagate further causing an "avalanche". To address this problem, we propose an efficient label propagation learning algorithm based on the graph-based regularization framework with adjusting network structure iteratively to improve the accuracy of classification algorithm for noisy data. We show empirically that this adjusting network structure improves significantly the performance of the algorithm for web page classification. In particular, we demonstrate that the proposed algorithm achieves good classification accuracy even for relatively large overlap across the classes.