Evaluation of Graph-based Algorithms for Guessing User Recommendations of the Social Network Instagram
Frank Martin Mehlhose, Michael Petrifke, Christoph Lindemann · 2021
In this paper, we present an analysis of the user recommendation algorithm of the popular social network Instagram. First, we introduce a framework for the analysis and evaluation of user recommendations in any social network. Then, we build a neighborhood graph for selected active users on Instagram, containing more than 609,000 users and 747,000 relationships. For these users, we also create a dataset of not publicly available personalized recommendation lists. Finally, we apply several graph-based algorithms to this graph to identify Instagram's user recommendation system. We show that an algorithm based on Twitter's Who-to-Follow algorithm is most similar to Instagram's unknown algorithm when recommending users connected to the followees of the target users. Moreover, this algorithm is very cost-effective due to requiring only a relatively small, bipartite graph, compared to the other candidates. Furthermore, we give an analysis of the composition of the user suggestions based on the different reasons for the suggestions.