Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
A Performance of Centrality Calculation in Social Networks
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transitive node similarity for link prediction in social networks with positive and negative links
Proceedings of the fourth ACM conference on Recommender systems
Using friendship ties and family circles for link prediction
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Exploiting longer cycles for link prediction in signed networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Predicting group evolution in the social network
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Identification of Group Changes in Blogosphere
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
| Hi-index | 0.00 |
Predicting the future direction of community evolution is a problem with high theoretical and practical significance. It allows to determine which characteristics describing communities have importance from the point of view of their future behaviour. Knowledge about the probable future career of the community aids in the decision concerning investing in contact with members of a given community and carrying out actions to achieve a key position in it. It also allows to determine effective ways of forming opinions or to protect group participants against such activities. In the paper, a new approach to group identification and prediction of future events is presented together with the comparison to existing method. Performed experiments prove a high quality of prediction results. Comparison to previous studies shows that using many measures to describe the group profile, and in consequence as a classifier input, can improve predictions.