Understanding task-driven information flow in collaborative networks

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 networksa problem that is important in practice yet difficult to solve without the method proposed in this paper.

Organizational Information Dissemination Within Collaborative Networks Using Digital Communication Tools

“…Personal networks are composed of people with whom a person maintains contact, including partners, customers, suppliers, and family members [8]. Public social networks focus on social interactions and social information exchange [23]. The structural properties of a social network are exhibited in an online network [16].…”

“…Collaborative networks are types of social networks that are formed by members who work together to achieve specific goals [23]. These types of networks have significantly different characteristics from the public social networks, since they are subject to organizational constraints that make the knowledge sharing process different from that of a public social network [7], [23].…”

“…These types of networks have significantly different characteristics from the public social networks, since they are subject to organizational constraints that make the knowledge sharing process different from that of a public social network [7], [23]. As a result, there is a need to statistically investigate the social influences of utilizing a unified communication tool to share knowledge within a private collaborative social network.…”

Expertise-Based Data Access in Content-Centric Mobile Opportunistic Networks

“…To address these problems, Miao et al [16] developed a more comprehensive model using both the content and the routing sequence of the ticket, which leads to better ticket forwarding decisions. Later in [17], they also studied the properties of collaborative networks (such as node degree distribution), and proposed a simpler model to capture how the tickets are routed by human beings. However, none of them consider the node queuing delay, and so the nodes with high expertise may be overloaded.…”

“…Stochastic Greedy Routing (SGR) [17]: SGR captures the behavior of humans in forwarding queries. The intuition is that, a query should be forwarded to a node with the following conditions: (i) it has closer expertise to that required by the query, (ii) it has higher node degree, assuming that a betterconnected neighbor is more likely to route the query along a shorter path to the node with closer expertise.…”