Understanding Task-driven Information Flow in Collaborative Networks

Publication Type

Conference Proceeding Article

Publication Date

4-2012

Abstract

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.

Keywords

Information Flow, Collaborative Networks, Social Routing

Discipline

Software Engineering

Research Areas

Software Systems

Publication

Proceedings of the 21st International Conference on World Wide Web Conference (WWW '12)

First Page

849

Last Page

858

ISBN

9781450312295

Identifier

10.1145/2187836.2187951

Publisher

ACM

City or Country

Lyon, France

Additional URL

http://dx.doi.org/10.1145/2187836.2187951

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