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
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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How the information diffuses over a large social network depends on both the model employed to simulate the diffusion and the network structure over which the information diffuses. We analyzed both theoretically and empirically how the two contrasting most fundamental diffusion models, Independent Cascade (IC) and Linear Threshold (LT) behave differently or similarly over different network structures. We devised two rewiring structures, one preserving in/out-degree correlation and the other changing in/out-degree correlation while both preserving their in/out-degree distributions, and analyzed how co-link rate and in/out-degree correlation affect the influence degree of each diffusion model using two real world networks, each as the base network on which rewiring is imposed. The results of the theoretical analysis qualitatively explain the empirical results, and the findings help deepen the understanding of complex diffusion phenomena.