Over the last decade, the Web has grown exponentially in size. Unfortunately, the number of incorrect, spamming, and malicious sites has also grown rapidly. Despite of that, users continue to rely on the search engines to separate the good from the bad, and rank results in such a way the best pages are suggested first. The probably most prominent ranking methods is PageRank [4]. Although Google...

Different models have been proposed for improving the results of Web search by taking into account the link structure of the Web. The PageRank algorithm models the behavior of a random surfer alternating between random jumps to new pages and following out links with equal probability. We propose to improve on PageRank by using an intelligent surfer that combines link structure and content to de...

In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively using homomorphic encryption based on Paillier scheme. In the proposed PageRank computation, a user encrypt its own graph data using asymmetric encryption method, sends the data set into different parties in a privacy-...

We give an improved local partitioning algorithm using heat kernel pagerank, a modified version of PageRank. For a subset S with Cheeger ratio (or conductance) h, we show that there are at least a quarter of the vertices in S that can serve as seeds for heat kernel pagerank which lead to local cuts with Cheeger ratio at most O( √ h), improving the previously bound by a factor of p log |S|.

— Anomaly detection techniques are widely used in a various type of applications. We explored proximity graphs for anomaly detection and the Page Rank algorithm. We used a different PageRank algorithm at peak in proximity graph collection of data points indicated by vertices, gives results a score quantifying the extent to which each data point is anomalous. In this way we requires first formin...

Community detection has be widely studied in the research of network analysis, and there are tens of existing tools and techniques to perform this, such as Fielder community (Fiedler 1973), Personalized PageRank algorithm (Andersen et al. 2006), and so on. Most of the previous works aim at finding the subset of nodes that has less incoming and outgoing edges than the edges within the subsets. H...

The concept of pagerank was first started as a way for determining the ranking of Webpages by Web search engines. Based on relations in interconnected networks, pagerank has become a major tool for addressing fundamental problems arising in general graphs, especially for large information networks with hundreds of thousands of nodes. A notable notion of pagerank, introduced by Brin and Page and...

PageRank is one of the most popular measures for ranking the nodes of a network according to their importance. However, PageRank is defined as a steady state of a random walk, which implies that the underlying network needs to be fixed and static. Thus, to extend PageRank to networks with a temporal dimension, the available temporal information has to be judiciously incorporated into the model....

Google uses the PageRank algorithm to determine the relative importance of a website. Link spamming is the name for putting links between websites with no other purpose than to increase the PageRank value of a website. To give a fair result to a search query it is important to detect whether a website is link spammed so that it can be filtered out of the search result. While the dominant eigenv...