Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Mining the network value of customers
Proceedings of the seventh 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
Power-Law Distributions in Empirical Data
SIAM Review
Measurement-calibrated graph models for social network experiments
Proceedings of the 19th international conference on World wide web
Generative models for ticket resolution in expert networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Information spreading in context
Proceedings of the 20th international conference on World wide web
Models of human navigation in information networks based on decentralized search
Proceedings of the 24th ACM Conference on Hypertext and Social Media
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Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers' problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks -- a problem that is important in practice yet difficult to solve without the method proposed in this paper.