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Purpose This paper aims to explore the characteristics of knowledge diffusion of library and information science to reveal its development trend and influence on other disciplines. Design/methodology/approach Based on the ESI discipline classification, this paper measures the knowledge diffusion from the library and information science to other disciplines over the last 24 years using indicators in four dimensions: breadth, intensity, speed and theme of knowledge diffusion. Findings The results show that the knowledge diffusion breadth of library and information science is wide, spreading to 21 ESI disciplines; the knowledge spread mainly concentrates in four soft or applied disciplines, and yet partially inter-disciplinary, and the knowledge diffusion intensity to each ESI discipline is parabolic whose highest point is mostly in 2004–2005; the speed of spreading to the 21 ESI disciplines is faster and faster, and the articles at the highest speed of knowledge diffusion are basically published after 2005; the knowledge diffusion themes are becoming increasingly diverse, deepening and specialization over time. Originality/value This paper modifies the relevant indicators of knowledge diffusion and constructs a measurement framework of knowledge diffusion from four aspects: breadth, intensity, speed and theme. The research method can also be used to explore the characteristics of knowledge absorption of a discipline from other ones.

Purpose Citation analysis is an important measure for the assessment of quality and impact of academic entities (authors, papers and publication venues) used for ranking of research articles, authors and publication venues. It is a common observation that high-level publication venues, with few exceptions (Nature, Science and PLOS ONE), are usually topic specific. The purpose of this paper is to investigate the claim correlation analysis between topic specificity and citation count of different types of publication venues (journals, conferences and workshops). Design/methodology/approach The topic specificity was calculated using the information theoretic measure of entropy (which tells us about the disorder of the system). The authors computed the entropy of the titles of the papers published in each venue type to investigate their topic specificity. Findings It was observed that venues usually with higher citations (high-level publication venues) have low entropy and venues with lesser citations (not-high-level publication venues) have high entropy. Low entropy means less disorder and more specific to topic and vice versa. The input data considered here were DBLP-V7 data set for the last 10 years. Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other. Originality/value This paper is the first attempt to discover correlation between topic sensitivity and citation counts of publication venues. It also used topic specificity as a feature to rank academic entities.

Purpose The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis. Design/methodology/approach In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship. Findings The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship. Research limitations/implications Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data. Practical implications The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining. Originality/value This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.