Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Emergent mating topologies in spatially structured genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Approximating betweenness centrality
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering coefficient queries on massive dynamic social networks
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
The role of social networks in information diffusion
Proceedings of the 21st international conference on World Wide Web
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A lot of research efforts have been made to model the diffusion process in social networks that varies from adoption of products in marketing strategies to disease and virus spread. Previously, a diffusion process is usually considered as a single-objective optimization problem, in which different heuristics or approximate algorithms are applied to optimize an objective of spreading single piece of information that captures the notion of diffusion. However, in real social networks individuals simultaneously receive several pieces of information during their communication. Single-objective solutions are inadequate for collective spread of several information pieces. Therefore, in this paper, we propose a Multi-Objective Diffusion Model (MODM) that allows the modeling of complex and nonlinear phenomena of multiple types of information exchange, and calculate the information worth of each individual from different aspects of information spread such as score, influence and diversity. We design evolutionary algorithm to achieve the multi-objectives in single diffusion process. Through extensive experiments on a real world data set, we have observed that MODM leads to a richer and more realistic class of diffusion model compared to a single objective. This signifies the correlation between the importance of each individual and his information processing capability. Our results indicate that some individuals in the network are naturally and significantly better connected in terms of receiving information irrespective of the starting position of the diffusion process.