The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Statistical Analysis of Network Data: Methods and Models
Statistical Analysis of Network Data: Methods and Models
Power-Law Distributions in Empirical Data
SIAM Review
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
Networks: An Introduction
Using provenance to extract semantic file attributes
TAPP'10 Proceedings of the 2nd conference on Theory and practice of provenance
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
The Open Provenance Model core specification (v1.1)
Future Generation Computer Systems
The W3C PROV family of specifications for modelling provenance metadata
Proceedings of the 16th International Conference on Extending Database Technology
Efficient budget allocation with accuracy guarantees for crowdsourcing classification tasks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.