We explore the relationship between official rankings of professional tennis players and rankings computed using a variant of the PageRank algorithm as proposed by Radicchi in 2011. We show Radicchi’s equations follow a natural interpretation of the PageRank algorithm and present up-to-date comparisons of official rankings with PageRankbased rankings for both the Association of Tennis Professio...

We present a novel technique for speeding up the computation of PageRank, a hyperlink-based estimate of the “importance” of Web pages, based on the ideas presented in [7]. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Power ...

• Ranking tweets in Twitter: To use PageRank for ranking tweets in Twitter we can construct a synthetic graph as follows. Represent each user and each tweet by a node. Draw a directed link from user A to B if A follows B. Also, draw a directed edge from a user A to a tweet t if A tweets or retweets t (see Figure 1). Now we can apply PageRank algorithm on this graph to obtain a ranking for tweet...

We present a new and efficient algorithm for determining a consensus value for a network of agents. Different from existing algorithms, our algorithm evaluates the consensus value for very large networks using heat kernel pagerank. We consider two frameworks for the consensus problem, a weighted average consensus among all agents, and consensus in a leader-following formation. Using a heat kern...

Reengineering and integrated development platforms typically do not list search results in a particularly useful order. PageRank is the algorithm prominently used by the Google internet search engine to rank the relative importance of elements in a set of hyperlinked documents. To determine the relevance of objects, classes, attributes, and methods we propose to apply PageRank to software artif...

Personalized PageRank expresses backlink-based page quality around user-selected pages in a similar way as PageRank expresses quality over the entire Web. Existing personalized PageRank algorithms can however serve on-line queries only for a restricted choice of page selection. In this paper we achieve full personalization by a novel algorithm that computes a compact database of simulated rando...

Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from exte...

In this paper, we consider the problem of calculating fast and accurate approximations to the personalized PageRank score ([8, 16]) of a webpage. We focus on techniques to improve speed by limiting the amount of webgraph data we need to access. PageRank scores are mainly used for ranking purposes, and generally only the scores exceeding a given threshold are relevant. In practice, and relative ...