Graph drawing by force-directed placement
Software—Practice & Experience
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Boosting algorithm with sequence-loss cost function for structured prediction
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Multidimensional Social Network in the Social Recommender System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
Label-dependent node classification in the network
Neurocomputing
Leveraging Network Properties for Trust Evaluation in Multi-agent Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Where's the Money? The Social Behavior of Investors in Facebook's Small World
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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A new method of feature extraction in the social network for within-network classification is proposed in the paper. The method provides new features calculated by combination of both: network structure information and class labels assigned to nodes. The influence of various features on classification performance has also been studied. The experiments on real-world data have shown that features created owing to the proposed method can lead to significant improvement of classification accuracy.