A social model for Literature Access: Towards a weighted social network of authors

Abstract

This paper presents a novel retrieval approach for litera-ture access based on social network analysis. In fact, we investigate a social model where authors represent the main entities and relationships are extracted from co-author and citation links. Moreover, we define a weighting model for so-cial relationships which takes into account the authors posi-tions in the social network and their mutual collaborations. Assigned weights express influence, knowledge transfer and shared interest between authors. Furthermore, we estimate document relevance by combing the document-query sim-ilarity and the document social importance derived from corresponding authors. To evaluate the effectiveness of our model, we conduct a series of experiments on a scientific document dataset that includes textual content and social data extracted from the academic social network CiteU-Like. Final results show that the proposed model improves the retrieval effectiveness and outperforms traditional and social information retrieval baselines.