Search Engine-inspired Ranking Algorithm for Trading Networks

Abstract

Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these differences to create a ranking algorithm for trading networks. We test our proposed method with international trading data from United Nations. The similarity measures between vectors of proposed algorithm and vector of standard measure give promising results.