27,826 research outputs found
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
Role of Ranking Algorithms for Information Retrieval
As the use of web is increasing more day by day, the web users get easily
lost in the web's rich hyper structure. The main aim of the owner of the
website is to give the relevant information according their needs to the users.
We explained the Web mining is used to categorize users and pages by analyzing
user's behavior, the content of pages and then describe Web Structure mining.
This paper includes different Page Ranking algorithms and compares those
algorithms used for Information Retrieval. Different Page Rank based algorithms
like Page Rank (PR), WPR (Weighted Page Rank), HITS (Hyperlink Induced Topic
Selection), Distance Rank and EigenRumor algorithms are discussed and compared.
Simulation Interface has been designed for PageRank algorithm and Weighted
PageRank algorithm but PageRank is the only ranking algorithm on which Google
search engine works.Comment: Keywords: Page Rank, Web Mining, Web Structured Mining, Web Content
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