SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Networks: An Introduction
Information Propagation Analysis in a Social Network Site
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Shortest Path Discovery in the Multi-layered Social Network
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Foundations of Multidimensional Network Analysis
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
The ML-Model for Multi-layer Social Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Community mining from multi-relational networks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Social network data and practices: the case of friendfeed
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Pareto distance for multi-layer network analysis
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Pareto distance for multi-layer network analysis
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Proceedings of the 23rd international conference on World wide web
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While most research in Social Network Analysis has focused on single networks, the availability of complex on-line data about individuals and their mutual heterogenous connections has recently determined a renewed interest in multi-layer network analysis. To the best of our knowledge, in this paper we introduce the first network formation model for multiple networks. Network formation models are among the most popular tools in traditional network studies, because of both their practical and theoretical impact. However, existing models are not sufficient to describe the generation of multiple networks. Our model, motivated by an empirical analysis of real multi-layered network data, is a conservative extension of single-network models and emphasizes the additional level of complexity that we experience when we move from a single- to a more complete and realistic multi-network context.