Ranking authors is vital for identifying a researcher’s impact and his standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The Author-Conf...

The PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that the PageRank obeys a ‘power law’ with the same exponent as the In-Degree. This paper presents a novel mathematical model that explains this phenomenon. The relation between the PageRank and In-Degree is modelled through a stochastic equation, which is inspired by the original definition of the Pa...

PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that PageRank values obey a power law with the same exponent as In-Degree values. This paper presents a novel mathematical model that explains this phenomenon. The relation between PageRank and In-Degree is modeled through a stochastic equation, which is inspired by the original definition of PageRank, an...

The PageRank equation computes the importance of pages in a web graph relative to a single random surfer with a constant teleportation coefficient. To be globally relevant, the teleportation coefficient should account for the influence of all users. Therefore, we correct the PageRank formulation by modeling the teleportation coefficient as a random variable distributed according to user behavio...

In AI and Web communities, many applications utilize PageRank. To obtain high PageRank score nodes, the original approach iteratively computes the PageRank score of each node until convergence by using the whole graph. If the graph is large, this approach is infeasible due to its high computational cost. The goal of this study is to find top-k PageRank score nodes efficiently for a given graph ...

This paper examines the fundamental problem of identifying most important nodes in a network. To date, more than hundred centrality measures have been proposed, each evaluating position node network from different perspective. Our work focuses on PageRank which is one computer science used wide range scientific applications. build theoretical foundation for choosing (or rejecting) specific sett...

This paper deals with the various changes that can be made to the basic PageRank model. We document the recent findings and add a few new contributions. These contributions concern (1) the sensitivity of the PageRank vector, (2) another method of forcing the Markov chain to be irreducible, and (3) a proof of the full spectrum of the PageRank matrix.