Browsing by Author "R, Hema Latha"

Now showing 1 - 3 of 3

AN ANALYSIS OF LINK STRENGTH IN SOCIAL NETWORKS
(International Journal of Engineering Research & Technology, 2012-11) R, Hema Latha; K, SathiyaKumari
A social structure made of nodes that are generally individuals or organizations. A social network represents relationships and flows between people, groups, organizations, animals, computers or other information or knowledge processing entities. Social networking websites allow users to be part of a virtual community. Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Link prediction in Facebook and Twitter can be done at a familiar class of graph generation model, where the nodes are united with locations in a latent metric space and connections are most likely between closer nodes. In this paper, GEPHI and NODEXL tools are used for the comparison measures to predict betweenness centrality of particular users account in Facebook and Twitter.
PREDICTING LINK STRENGTH IN ONLINE SOCIAL NETWORKS
(International Journal of Engineering Research and Applications, 2012-12) R, Hema Latha; K, Sathiyakumari
Social Media is a term that encompasses the platforms of New Media, but also implies the inclusion of systems like Facebook, and other things typically thought of as social networking. The idea is that they are media platforms with social components and public communication channels. Social media are primarily Internetbased tools for sharing and discussing information among human beings. Data mining (the analysis step of the “Knowledge Discovery in Databases” process, or KDD), is the process that attempts to discover patterns in large data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. It involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, postprocessing of discovered structures, visualization, and online updating. Link prediction in Facebook and Twitter can be done at a familiar class of graph generation model, where the nodes are united with locations in a latent metric space and connections are more likely between closer nodes. In this paper, Gephi tool is used to predict the link of Facebook.
SURVEY ON LINK PREDICTION IN FACEBOOK AND TWITTER
(International Journal of Engineering Research and Applications, 2012-09) R, Hema Latha; K, SathiyaKumari
Social media have gained increased usage rapidly for a discrepancy of reasons. Participants were asked to view one of six mock Twitter.com pages that varied both the number of followers and the ratio between followers and follows on the page and report their perceived source worthy of trust. This research examines mediocre factors and speculative relevant contextual variables that affect sensational formation and the willingness to initiate virtual friendship. In this paper [ Network Analysis of Recurring YouTube Spam Campaigns] demonstrated tracking of spam campaigns over time using network motif profiling. Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. In this paper [Learning to blend vitality rankings from heterogeneous social networks], demonstrated the problem of blending vitality denotes all kinds of updates user receives in various social networks. It proposed variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, employed divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network respectively.