Survey on Link Prediction and Page Ranking In Blogs S.Geetha

This paper presents a study of the various aspects of link prediction and page ranking in blogs. Social networks have taken on a new eminence from the prospect of the analysis of social networks, which is a recent area of research which grew out of the social sciences as well as the exact sciences, especially with the computing capacity for mathematical calculations and even modelling which was previously impossible. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. Link prediction has many applications, including recommending new items in online networks (e.g., products in eBay and Amazon, and friends in Face book), monitoring and preventing criminal activities in a criminal network, predicting the next web page users will visit, and complementing missing links in automatic web data crawlers. Page Rank is the technique used by Google to determine importance of page on the web. It considers all incoming links to a page as votes for Page Rank. Our findings provide an overview of social relations and we address the problem of page ranking and link prediction in networked data, which appears in many applications such as network analysis or recommended systems. Keywords- web log, social networks analysis, readership, link prediction, Page ranking. I

Dynamic Tagging for Enterprise Knowledge Sharing and Representation

The development of Web 2.0 technology provides an easy way for people to transfer and share knowledge. As a collaborative tagging tool, folksonomy is an efficient indexing method in Web 2.0 environments. After analyzing the pros and cons of current knowledge management systems, this research proposes a dynamic Tagging system that combines folksonomy technologies with other approaches including automatic schema enrichment and training. The proposed system improves access to a large, growing collection by supporting users collaboratively contribute to the building of tags. In addition, the proposed system provides an efficient way for firms to represent knowledge and share knowledge with customers and other firms

A Service Oriented Framework for Analysing Social Network Activities

AbstractAnalysing and monitoring Social Networking activities raise multiple challenges for the evolution of Service Oriented Systems Engineering. This is particularly evident for event detection in social networks and, more in general, for large-scale Social Analytics, which require continuous processing of data. In this paper we present a service oriented framework exploring effective ways to leverage the opportunities coming from innovations and evolutions in computational power, storage, and infrastructures, with particular focus on modern architectures including in-memory database technology, in-database computation, massive parallel processing, Open Data Services, and scalability with multi-node clusters in Cloud. A prototype of this system was experimented in the contest of a specific kind of social event, an art exhibition of sculptures, where the system collected and analyzed in real-time the tweets issued in an entire region, including exhibition sites, and continuously updated analytical dashboards placed in one of the exhibition rooms

Algorithmic Distortion of Informational Landscapes

The possible impact of algorithmic recommendation on the autonomy and free choice of Internet users is being increasingly discussed, especially in terms of the rendering of information and the structuring of interactions. This paper aims at reviewing and framing this issue along a double dichotomy. The first one addresses the discrepancy between users' intentions and actions (1) under some algorithmic influence and (2) without it. The second one distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement. In all cases, we focus on and differentiate situations where algorithms empirically appear to expand the cognitive and social horizon of users, from those where they seem to limit that horizon. We additionally suggest that these biases may not be properly appraised without taking into account the underlying social processes which algorithms are building upon