document: Understanding task-driven information flow in collaborative networks 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 .
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