Article Impact Value for Nearby Citation Network Analysis

In today's large and rapidly increasing amount of scientific publications, exploring recent studies in a given research area has become more difficult than any time before. As a result, authors and publishers rely on different factors to measure the relationships and the impact of their produced research work using parameters such as the impact factor, number of citations, co-citations, and others. In this paper, we propose an Article Impact Value (AIV) that enables us to identify the actual impact of each cited articles in a citation relationship. We utilize the article metadata to calculate a weighted content-based similarity between articles. We also utilize the proceedings and journals impact index into the computation of the AIV. We demonstrate, experimentally, an applicable algorithm that employs the publications impact factor and the citation counts to estimate the impact value of each cited article in a citation network.