Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive global illumination in dynamic participating media using selective photon tracing
Proceedings of the 21st spring conference on Computer graphics
Reinforcing Web-object Categorization Through Interrelationships
Data Mining and Knowledge Discovery
Relational Ensemble Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
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
Average Distance, Diameter, and Clustering in Social Networks with Homophily
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
| Hi-index | 0.00 |
Quality of collective inference relational graph classifier depends on a degree of homophily in a classified graph. If we increase homophily in the graph, the classifier would assign class-membership to the instances with reduced error rate. We propose to substitute traditionally used graph neighborhood method (based on direct neighborhood of vertex) with local graph ranking algorithm (activation spreading), which provides wider set of neighboring vertices and their weights. We demonstrate that our approach increases homophily in the graph by inferring optimal homophily distribution of a binary Simple Relational Classifier in an unweighted graph. We validate this ability also experimentally using the Social Network of the Slovak Companies dataset.