Hosting platforms for software projects can form collaborative social networks and a prime example of this is GitHub which is arguably the most popular platform of this kind. An open source project recommendation system could be a major feature for a platform like GitHub, enabling its users to find relevant projects in a fast and simple manner. We perform network analysis on a constructed graph based on GitHub data and present a recommendation system that uses link prediction.
A similar study performed developer collaboration analysis on a similar open source network @cite_10 , namely SourceForge. They obtained similar results, the average distance appears to be very low. They also extracted topological patterns @cite_10 , sub graphs of the original network. Through analysis of these patterns they discovered that the clustering coefficient is relatively high, i.e. the friends of a developer are likely to connect. Together with the low average distance, the SourceForge network appears to be small-world; however, we cannot infer the same for the GitHub network.
@cite_10
2030284415
In this study, we extract patterns from a large developer collaborations network extracted from Source Forge. Net at high and low level of details. At the high level of details, we extract various network-level statistics from the network. At the low level of details, we extract topological sub-graph patterns that are frequently seen among collaborating developers. Extracting sub graph patterns from large graphs is a hard NP-complete problem. To address this challenge, we employ a novel combination of graph mining and graph matching by leveraging network-level properties of a developer network. With the approach, we successfully analyze a snapshot of Source Forge. Net data taken on September 2009. We present mined patterns and describe interesting observations.