Predicting Social Network Measures Using Machine Learning Approach

Authors:
Radoslaw Michalski;Przemyslaw Kazienko;Dawid Krol
Affiliations:
-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 7
Cited 0

Connectivity and inference problems for temporal networks

STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The link prediction problem for social networks

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Link mining: a survey

ACM SIGKDD Explorations Newsletter
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Modelling social network evolution

SocInfo'11 Proceedings of the Third international conference on Social informatics
Scalable Link Prediction on Multidimensional Networks

ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops

Quantified Score

Hi-index	0.00

Visualization

Abstract

The link prediction problem in social networks defined as a task to predict whether a link between two particular nodes will appear in the future is still a broadly researched topic in the field of social network analysis. However, another relevant problem is solved in the paper instead of individual link forecasting: prediction of key network measures values, what is a more time saving approach. Two machine learning techniques were examined: time series forecasting and classification. Both of them were tested on two real-life social network datasets.