ANDMC: An Algorithm for Author Name Disambiguation Based on Molecular Cross Clustering

With the rapid development of information technology, the problem of name ambiguity has become one of the main problems in the fields of information retrieval, data mining and scientific measurement, which inevitably affects the accuracy of information calculations, reduces the credibility of the literature retrieval system, and affect the quality of information. To deal with this, name disambiguation technology has been proposed, which maps virtual relational networks to real social networks. However, most existing related work did not consider the problem of name coreference and the inability to correctly match due to the different writing formats between two same strings. This paper mainly proposes an algorithm for Author Name Disambiguation based on Molecular Cross Clustering (ANDMC) considering name coreference. Meanwhile, we explored the string matching algorithm called Improved Levenshtein Distance (ILD), which solves the problem of matching between two same strings with different writing format. The experimental results show that our algorithm outperforms the baseline method. (F1-score 9.48% 21.45% higher than SC and HAC).