Activity Dynamics in Collaboration Networks

TL;DR

This paper models activity dynamics in collaboration networks, analyzing how social influence and incentives affect system sustainability, and introduces a new metric to measure activity robustness.

Contribution

It presents a novel activity dynamics model for collaboration networks and introduces the Activity Momentum metric to evaluate their activity robustness.

Findings

Activity can vanish over time without incentives or external stimuli.

Proper manipulation of network dynamics can revive inactive systems.

The Activity Momentum metric effectively assesses network activity robustness.

Abstract

Many online collaboration networks struggle to gain user activity and become self-sustaining due to the ramp-up problem or dwindling activity within the system. Prominent examples include online encyclopedias such as (Semantic) MediaWikis, Question and Answering portals such as StackOverflow, and many others. Only a small fraction of these systems manage to reach self-sustaining activity, a level of activity that prevents the system from reverting to a non-active state. In this paper, we model and analyze activity dynamics in synthetic and empirical collaboration networks. Our approach is based on two opposing and well-studied principles: (i) without incentives, users tend to lose interest to contribute and thus, systems become inactive, and (ii) people are susceptible to actions taken by their peers (social or peer influence). With the activity dynamics model that we introduce in this paper we can represent typical situations of such collaboration networks. For example, activity in a collaborative network, without external impulses or investments, will vanish over time, eventually rendering the system inactive. However, by appropriately manipulating the activity dynamics and/or the underlying collaboration networks, we can jump-start a previously inactive system and advance it towards an active state. To be able to do so, we first describe our model and its underlying mechanisms. We then provide illustrative examples of empirical datasets and characterize the barrier that has to be breached by a system before it can become self-sustaining in terms of critical mass and activity dynamics. Additionally, we expand on this empirical illustration and introduce a new metric p---the Activity Momentum---to assess the activity robustness of collaboration networks.