Influence Maximization for Big Data Through Entropy Ranking and Min-Cut

Sancen-Plaza, Agustin and Mendez-Vazquez, Andres (2013) Influence Maximization for Big Data Through Entropy Ranking and Min-Cut. In: 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[thumbnail of 19057.pdf] PDF
19057.pdf
Download (1MB)

Abstract

As Big Data becomes prevalent, the traditional
models from Data Mining or Data Analysis, although very efficient, lack the speed necessary to process problems with data sets in the range of million samples. Therefore, the need for designing more efficient and faster algorithms for these new types o

Item Type: Conference or Workshop Item (UNSPECIFIED)
Date Deposited: 04 Mar 2026 10:06
Last Modified: 17 Apr 2026 20:47
URI: http://eprints.eai.eu/id/eprint/10662

Actions (login required)

View Item
View Item