✦ LIBER ✦

Data mining of social networks represented as graphs

✍ Scribed by Nettleton, David F.

Book ID: 120169931
Publisher: Elsevier
Year: 2013
Tongue: English
Weight: 880 KB
Volume: 7
Category: Article
ISSN: 1574-0137
DOI: 10.1016/j.cosrev.2012.12.001

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Line graphs as social networks

✍ M.J. Krawczyk; L. Muchnik; A. Mańka-Krasoń; K. Kułakowski 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 502 KB

[Advances in Database Systems] Managing

[Advances in Database Systems] Managing and Mining Graph Data Volume 40 || Graph Mining Applications to Social Network Analysis

✍ Aggarwal, Charu C.; Wang, Haixun 📂 Article 📅 2010 🏛 Springer US 🌐 English ⚖ 859 KB

Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studie

Social Network Data Analytics || Text Mi

Social Network Data Analytics || Text Mining in Social Networks

✍ Aggarwal, Charu C. 📂 Article 📅 2011 🏛 Springer US ⚖ 482 KB

Social Network Data Analytics || Data Mi

Social Network Data Analytics || Data Mining in Social Media

✍ Aggarwal, Charu C. 📂 Article 📅 2011 🏛 Springer US 🌐 English ⚖ 403 KB

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive stream

Computational Social Networks || Perform

Computational Social Networks || Performance Evaluation of Social Network Using Data Mining Techniques

✍ Abraham, Ajith 📂 Article 📅 2012 🏛 Springer London ⚖ 464 KB

R and Data Mining || Social Network Anal

R and Data Mining || Social Network Analysis

✍ Zhao, Yangchang 📂 Article 📅 2013 🏛 Elsevier 🌐 English ⚖ 506 KB

*R and Data Mining* introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amount