Multiple Comparisons in Induction Algorithms
Machine Learning
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A Visual Query Language for Relational Knowledge Discovery TITLE2:
A Visual Query Language for Relational Knowledge Discovery TITLE2:
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
A genetic algorithm-based method for feature subset selection
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Improving the classification of newsgroup messages through social network analysis
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Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Semi-supervised Classification from Discriminative Random Walks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Temporal-Relational Classifiers for Prediction in Evolving Domains
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Proceedings of the 18th international conference on World wide web
Genetic programming for protein related text classification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An evolutionary approach to constructive induction for link discovery
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Fuzzy relational classifier trained by fuzzy clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein---protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose a framework, Genetic Algorithm enhanced Time Varying Relational Classifier for evolving Heterogeneous Networks (GA-TVRC-Het), to extract the effects of different relationship types in different time periods in the past. These effects are discovered adaptively by utilizing genetic algorithms. A relational classifier is extended as the classification method in order to be able to work with different types of nodes. The proposed framework is tested on two real world data sets. It is shown that using the optimal time effect improves the classification performance to a large extent. It is observed that the optimal time effect does not necessarily follow a certain functional trend, for example linear or exponential decay in time. Another observation is that the optimal time effect may be different for each type of interaction. Both observations reveal the reason why GA-TVRC-Het outperforms methods that rely on a predefined form of time effect or the same time effect for each link type.