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
Tutorial on agent-based modeling and simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Diffusion dynamics in small-world networks with heterogeneous consumers
Computational & Mathematical Organization Theory
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward an interoperable dynamic network analysis toolkit
Decision Support Systems
Model alignment of anthrax attack simulations
Decision Support Systems - Special issue: Intelligence and security informatics
A note on maximizing the spread of influence in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Maximizing product adoption in social networks
Proceedings of the fifth ACM international conference on Web search and data mining
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In the era of web2.0, marketers are eager to benefit from viral advertising. In this paper we propose a computational network model of viral advertising to examine the maximization of influence within social networks. For our network model we combine both the independent cascade model and the threshold model. We use a spreading threshold to trigger the cascading process, to examine the ways in which advertisements spread across the social network. We also investigate the procedures for choosing an initial set of people to maximize the performance of advertisement spreading. Furthermore, we analyse the impact of network structures on the dynamics of diffusion, and a strategy for combining viral advertising with mass marketing in e-commerce. We also run simulations using a real dataset to check the diffusion of advertisements in an online social network. Ultimately we discovered that a combination of viral advertising and mass marketing is better to diffuse advertisements than either method wholly by itself. Using an optimal algorithm improves diffusion performance, but using 'degree' is also an alternative way of choosing initial nodes when the whole structure of network is unknown. Integrating simulations to build a real-time decision support platform will make the diffusion of advertisements more efficient.