text: Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.
  @cite_26: Human subjects perform a computationally wide range of tasks from only local, networked interactions.
  @cite_7: We study collaboration networks in terms of evolving, self-organizing bipartite graph models. We propose a model of a growing network, which combines preferential edge attachment with the bipartite structure, generic for collaboration networks. The model depends exclusively on basic properties of the network, such as the total number of collaborators and acts of collaboration, the mean size of collaborations, etc. The simplest model defined within this framework already allows us to describe many of the main topological characteristics (degree distribution, clustering coefficient, etc.) of one-mode projections of several real collaboration networks, without parameter fitting. We explain the observed dependence of the local clustering on degree and the degree‐degree correlations in terms of the “aging” of collaborators and their physical impossibility to participate in an unlimited number of collaborations.
  @cite_9: A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of experiments on Amazon Mechanical Turk, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial "seed" players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation did not spread beyond direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs.
  @cite_17: Wikipedia's success is often attributed to the large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the coordination needed to write an article collaboratively, adding contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors structure the work by doing the majority of it. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the "wisdom of the crowd" in online production environments.
  @cite_19: Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions.
  @cite_11: Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?"
summary: Networks of collaboration between scientists have been studied by Newman @cite_11 . The authors argue that simple unweighted networks are unable to capture the strength of collaboration ties and propose a method to model the strength of collaboration by relying on the number of co-authored papers. The same author has later studied various properties of such networks @cite_11 . @cite_7 have studied collaboration networks from an evolving and self-organizing perspective. Behavioral experiments on the ability to solve problems collaboratively have been conducted by Kearns @cite_26 . Online collaboration with different network topologies has been studied by Suri and Watts @cite_9 . The exploration-exploitation trade-off in a collaborative problem solving task has been discussed by Mason and Watts @cite_19 . Kittur and Kraut @cite_17 studied various types of collaboration taking place between Wikipedia editors and measured the impact on quality of the resulting articles.