The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
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
Personalized recommendation driven by information flow
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Diffusion dynamics in small-world networks with heterogeneous consumers
Computational & Mathematical Organization Theory
A New Product Growth for Model Consumer Durables
Management Science
Journal of the American Society for Information Science and Technology
The Impact of Structural Changes on Predictions of Diffusion in Networks
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The impact of countermeasure propagation on the prevalence of computer viruses
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Interaction-based HPC modeling of social, biological, and economic contagions over large networks
Proceedings of the Winter Simulation Conference
Simulating the Diffusion of Information: An Agent-Based Modeling Approach
International Journal of Agent Technologies and Systems
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Diffusion occurs in various contexts and generally involves a network of entities and interactions between entities. Through these interactions, some property, e.g. information, ideas, etc., is spread through the network. The network may become dynamic as entities in the network interact and information, ideas, etc. flow through the network. This paper presents a general model of diffusion in dynamic networks. We use the model to examine how network structure, seeding strategy, and population inhomogeneity as defined with trust, affects the diffusion process. We simulate an evacuation scenario where the network structure represents a network of households. There are multiple sources that initiate the broadcasts of evacuation warnings and the goals are for the households to propagate the message and perform evacuation. The network dynamics observed are the result of the diffusion, where households may leave the network some time after receiving the warning. The results provide interesting observations on the effects of trust asymmetry and trust differentials. When we introduce population inhomogeneity using trust, the diffusion was more effective. The network structure and the seeding strategy used in delivering the initial broadcast also affect the effectiveness of the diffusion.