Predicting Citation Counts for Academic Literature using Graph Pattern Mining

N. Pobiedina,R. Ichise:
"Predicting Citation Counts for Academic Literature using Graph Pattern Mining";
Vortrag: 27th International Conference on Industrial, Engineering&Other Applications of Applied Intelligent Systems, Kaohsiung, Taiwan; 03.06.2014 - 06.06.2014; in:"Modern Advances in Applied Intelligence", Springer Verlag (Hrg.); Lecture Notes in Computer Science, (2014), S. 109 - 119.

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The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the
basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.