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able to rank award winners at the top of the list accurately, and outperformed both the standard P R and the h-index. 5.3 Contrary Findings In a 2014 study by Fiala, Šubelj, Žitnik, and Bajec (2015), they took three large datasets of papers in the computer science fields and analyzed them us- ing a variety of different ranking methods. Fiala et al. (2015) compared 12 dif- ferent methods, including citation count, in-degree, where they only count one citation from each author, and HITS, a method similar to PageRank. Fiala et al. (2015) also compared the standard PageRank algorithm and alterations of the algorithm, using various methods to weigh it, taking into account the number of citations, collaborations, and common publications between authors. Fiala et al. (2015) used a modified version of the PageRank algorithm: (v,j)εE (1 − d) ∑ PR(v)Ω (V.2) PR(u)= |V| +d (v,u)∈E where Ω = σv,k/ ∑ wv,j, and σv,k= wv,k ∑ (cv,k + 1)/(bv,k + 1) (v,j)∈E wv,j (V.3) where V is the set of authors, E is the set of edges, and w, b, and c are coeffi- cients used to produce a weight for each citation. The coefficients are defined where w is the number of citations between authors, c is the number of collabo- rations between authors, and b is the number of common publications between them. By adjusting the values of those coefficients, Fiala et al. (2015) obtained a variety of algorithms to work with. Fiala et al. (2015) compared the results from their rankings to the editorial board membership of prestigious journals. After analyzing their data sets for each variation, they concluded that the PageRank 26PDF Image | MATHEMATICS BEHIND GOOGLE PAGERANK ALGORITHM
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