Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An algorithm for drawing general undirected graphs
Information Processing Letters
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Propositionalization approaches to relational data mining
Relational Data Mining
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Efficient inference with cardinality-based clique potentials
Proceedings of the 24th international conference on Machine learning
Editorial: Hybrid learning machines
Neurocomputing
Within-Network Classification Using Local Structure Similarity
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An evolutionary approach to query-sampling for heterogeneous systems
Expert Systems with Applications: An International Journal
User position measures in social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Label-dependent feature extraction in social networks for node classification
SocInfo'10 Proceedings of the Second international conference on Social informatics
A method of label-dependent feature extraction in social networks
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relational large scale multi-label classification method for video categorization
Multimedia Tools and Applications
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Relations between objects in various systems, such as hyperlinks connecting web pages, citations of scientific papers, conversations via email or social interactions in Web 2.0 portals are commonly modeled by networks. One of many interesting problems currently studied for such domains is node classification. Due to the nature of the networked data and the unavailability of collection of nodes' broad representation for training in majority of environments, only a very limited data may remain useful for classification. Therefore, there is a need for accurate and efficient algorithms that are able to perform good classification based only on scanty knowledge of network nodes. A new approach of sampling algorithm-LDGibbs, used in the context of collective classification with application of label-dependent features, is proposed in the paper in order to provide more accurate generalization for sparse datasets. Additionally, a new LDBootstrapping algorithm based on label-dependent features has been developed. Both new algorithms include additional steps to extract new input features based on graph structures but limited only to the nodes of a given label. It means that a separate set of structural features is provided for each label. The comparison with the other approaches, in particular with standard Gibbs Sampling and bootstrapping provided satisfactory results and revealed LDGibbs's superiority.