Efficient identification of Web communities
Proceedings of the sixth 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
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Cooperation in Evolving Social Networks
Management Science
Incentive Rewarding Method for Information Propagation in Social Networks
SAINT '10 Proceedings of the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet
A data-based approach to social influence maximization
Proceedings of the VLDB Endowment
Minimizing Seed Set for Viral Marketing
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Influence maximizing and local influenced community detection based on multiple spread model
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Targeting online communities to maximise information diffusion
Proceedings of the 21st international conference companion on World Wide Web
Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks
CGC '12 Proceedings of the 2012 Second International Conference on Cloud and Green Computing
The multidimensional study of viral campaigns as branching processes
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Towards Maximising Cross-Community Information Diffusion
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Large Social Networks Can Be Targeted for Viral Marketing with Small Seed Sets
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to compensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.