By Topic

Social network analysis algorithm on a many-core GPU

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sang Won Seo ; Sch. of Comput. Sci., Kookmin Univ., Seoul, South Korea ; Joohyun Kyong ; Eun-Jin Im

Proliferation of social network service permeates lives of people on internet, and its social, political and cultural significance prompts the need to understand and to analyze the contents and structures of such services. The sheer volume of such social network is enormous, and it necessitates development and implementation of efficient social network analysis algorithms. Among these, Influence Maximization is one example of such algorithm. The objective of the influence maximization algorithm is to find a small subset of nodes, so-called seed-nodes, that result in maximization of the spread of influence through the edges in the graph which represents connections in social network. As the cost-efficient, high-performance computing power of many-core GPUs is widely utilized in nearly all areas of computing, we apply our expertise in GPU parallelization to the influence maximization algorithm. The graph algorithms are known as one of sparse algorithms since its irregular data structure requires indirect accesses to memory, resulting in lowbandwidth memory access. Sparse algorithms are one area where many researchers are focused on its efficient parallelization, because the usage of such algorithms is universal and thus, vital to broad application areas, from scientific simulations to social studies. In this paper, we introduce algorithms to compute influence maximization of social network, and adopt this algorithm to fit parallel implementation on many-core GPU. We also analyze our implementations in terms of factors affecting GPU performance.

Published in:

Ubiquitous and Future Networks (ICUFN), 2012 Fourth International Conference on

Date of Conference:

4-6 July 2012