Artificial Intelligence Related Publication Analysis Based on Citation Counting

Artificial intelligence is one of the most popular technologies in recently years. Journals and conferences are widely viewed as major tools to track the development of technologies. Citation counting analysis is one of the most acknowledged metrics in spite of its controversial drawbacks. To the best of our knowledge, most methods based on citation counting do not taken into account the citation weight in different years. In this paper, we focused on citation counting and designed a scheme to calculate both the citation weight and weighting of the cited credits of different publications, which are used to verify the efficiency of the proposed scheme. We also evaluated the popularity of publications by calculating their popularity scores. Unlike other ranking regulations, our proposed measure was able to compare journals and conferences simultaneously. In addition, we extracted ranking results to calculate the pairwise similarity via a generalized measure, which provided a more objective insight into the differences between publications. Several interesting observations were found from the experimental results with real data.