The Library
Topic-based influence computation in social networks under resource constraints
Tools
Bingol, Kaan, Eravci, Bahaeddin, Etemoglu, Cagri Ozgenc, Ferhatosmanoglu, Hakan and Gedik, Bugra (2016) Topic-based influence computation in social networks under resource constraints. IEEE Transactions on Services Computing . p. 1. doi:10.1109/TSC.2016.2619688 ISSN 1939-1374.
|
PDF
WRAP-topic-based-influence-computation-social-Ferhatosmanoglu-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1330Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TSC.2016.2619688
Abstract
As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy).
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Microblogs -- Mathematical models, Twitter (Firm), Algorithms | ||||||
Journal or Publication Title: | IEEE Transactions on Services Computing | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1939-1374 | ||||||
Official Date: | 21 October 2016 | ||||||
Dates: |
|
||||||
Page Range: | p. 1 | ||||||
DOI: | 10.1109/TSC.2016.2619688 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 29 September 2017 | ||||||
Date of first compliant Open Access: | 29 September 2017 | ||||||
Funder: | Türkiye Bilimler Akademisi, Türk Telekom |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year