Who to follow recommendation in large-scale online development communities

Abstract

ContextOpen source development allows a large number of people to reuse and contribute source code to the community. Social networking features open opportunities for information discovery, social collaborations, and improved recommendations of potential collaborators. ObjectiveOnline community and development platforms rely on social network features to increase awareness and attention among community members for improved collaborations. The objective of this work is to introduce an approach for recommending relevant users to follow. Follower networks provide means for informal information propagation. The efficiency and effectiveness of such information flows is impacted by the network structure. Here, we aim to understand the resilience of networks against random or strategic node removal. MethodSocial network features of online software development communities present a new opportunity to enhance online collaboration. Our approach is based on the automatic analysis of user behavior and network structure. The proposed ‘who to follow’ recommendation algorithm can be parametrized for specific contexts. Link-analysis techniques such as PageRank/HITS provide the basis for a novel ‘who to follow’ recommendation model. ResultsWe tested the approach using a GitHub-based dataset. Currently, users follow popular community members to get updates regarding their activities instead of maintaining personal relations. Thus, social network features require further improvements to increase reciprocity. The application of our ‘who to follow’ recommendation model using the GitHub dataset shows excellent results with respect to context-sensitive following recommendations. The sensitivity of GitHub’s follower network to random node removal is comparable with other social networks but more sensitive to follower authority based node removal. ConclusionLink-based algorithm can be used for context-sensitive ‘who to follow’ recommendations. GitHub is highly sensitive to authority based node removal. Information flow established through follower relations will be strongly impacted if many authorities are removed from the network. This underpins the importance of ‘central’ users and the validity of focusing the ‘who to follow’ recommendations on those users.