Identification of influential social networkers

Authors:
Magdalini Eirinaki;Sumit Pal Singh Monga;Shreedhar Sundaram
Affiliations:
Computer Engineering Department, San Jose State University, San Jose, CA, 95192 0180, USA.;Computer Engineering Department, San Jose State University, San Jose, CA, 95192 0180, USA.;Computer Engineering Department, San Jose State University, San Jose, CA, 95192 0180, USA
Venue:
International Journal of Web Based Communities
Year:
2012

Citing 22
Cited 0

Mining the network value of customers

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Recommender Systems Research: A Connection-Centric Survey

Journal of Intelligent Information Systems
BlogRank: ranking weblogs based on connectivity and similarity features

AAA-IDEA '06 Proceedings of the 2nd international workshop on Advanced architectures and algorithms for internet delivery and applications
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Community Mining from Signed Social Networks

IEEE Transactions on Knowledge and Data Engineering
Identifying opinion leaders in the blogosphere

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Identifying the influential bloggers in a community

WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predicting trusts among users of online communities: an epinions case study

Proceedings of the 9th ACM conference on Electronic commerce
The New Influencers: A Marketer's Guide to the New Social Media

The New Influencers: A Marketer's Guide to the New Social Media
Community gravity: measuring bidirectional effects by trust and rating on online social networks

Proceedings of the 18th international conference on World wide web
Trust and nuanced profile similarity in online social networks

ACM Transactions on the Web (TWEB)
An Analytical Way to Find Influencers on Social Networks and Validate their Effects in Disseminating Social Games

ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Extracting influential nodes for information diffusion on a social network

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Identifying Influential Bloggers: Time Does Matter

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Personalised and dynamic trust in social networks

Proceedings of the third ACM conference on Recommender systems
Mining Influential Bloggers: From General to Domain Specific

KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
TwitterRank: finding topic-sensitive influential twitterers

Proceedings of the third ACM international conference on Web search and data mining
Towards a Personalized Blog Site Recommendation System: A Collaborative Rating Approach

SMAP '09 Proceedings of the 2009 Fourth International Workshop on Semantic Media Adaptation and Personalization
User position measures in social networks

Proceedings of the 3rd Workshop on Social Network Mining and Analysis
A Study on Social Network Metrics and Their Application in Trust Networks

ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Online social networking is deeply interleaved in today's lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange information, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate in this type of marketing could be increased by targeting specific individuals, called 'influential users', having the largest possible reach within an online community. In this paper, we present a method aiming at identifying the influential users within an online social networking application. We introduce ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence. We then assess this algorithm's added value in identifying influential users compared to other commonly used social network analysis metrics, such as the betweenness centrality and the well-known PageRank, by performing an experimental evaluation on a synthetic and a real-life dataset. We also integrate all three metrics in a unified metric and measure its performance.