Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

The increasing availability of large-scale protein-protein interaction data has made it
possible to understand the basic components and organization of cell machinery from the …

Google's PageRank method was developed to evaluate the importance of web-pages via
their link structure. The mathematics of PageRank, however, are entirely general and apply …

In this paper, we first extend the celebrated PageRank modification to a higher-order Markov
chain. Although this system has attractive theoretical properties, it is computationally …

Motivation: Diffusion-based network models are widely used for protein function prediction
using protein network data and have been shown to outperform neighborhood-based and …

Background Characterization of unknown proteins through computational approaches is
one of the most challenging problems in silico biology, which has attracted world-wide …

The PageRank algorithm is widely considered these years because of its great significance
in search engine technology and other scientific domains. Though the power method is the …

Motivation Metabolomics has shown great potential to improve the understanding of
complex diseases, potentially leading to therapeutic target identification. However, no single …

PageRank computes the importance of each node in a directed graph under a random
surfer model governed by a teleportation parameter. Commonly denoted alpha, this …

We suggest a revision to the PageRank random surfer model that considers the influence of
a population of random surfers on the PageRank vector. In the revised model, each member …