Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Discovering Test Set Regularities in Relational Domains
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Exploiting relational structure to understand publication patterns in high-energy physics
ACM SIGKDD Explorations Newsletter
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
WSC '04 Proceedings of the 36th conference on Winter simulation
Empirical comparison of "hard" and "soft" label propagation for relational classification
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Iterative relational classification through three–state epidemic dynamics
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Efficient label propagation for classification on information networks
Proceedings of the Third Symposium on Information and Communication Technology
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In this paper we consider dynamical properties of simple iterative relational classifiers. We conjecture that for a class of algorithms that use label-propagation the iterative procedure can lead to nontrivial dynamics in the number of newly classified instances. The underlaying reason for this nontriviality is that in relational networks true class labels are likely to propagate faster than false ones. We suggest that this phenomenon, which we call two-tiered dynamics for binary classifiers, can be used for establishing a self-consistent classification threshold and a criterion for stopping iteration. We demonstrate this effect for two unrelated binary classification problems using a variation of a iterative relational neighbor classifier. We also study analytically the dynamical properties of the suggested classifier, and compare its results to the numerical experiments on synthetic data.