Distinguishing Homophily from Peer Influence Through Network Representation Learning

Peer influence and homophily are two entangled forces underlying social influences. However, distinguishing homophily from peer influence is difficult, particularly when there is latent homophily caused by unobservable features. This paper proposes a novel data-driven framework that combines the advantages of latent homophily identification and causal inference. Specifically, the approach first utilizes scalable network representation learning algorithms to obtain node embeddings, which are extracted from social network structures. Then, the embeddings are used to control latent homophily in a quasi-experimental design for causal inference. The simulation experiments show that the proposed approach can estimate peer influence more accurately than existing parameterized approaches and data-driven methods. We applied the proposed framework in an empirical study of players’ online gaming behaviors. First, our approach can achieve improved model fitness for estimating peer influence in online games. Second, we discover a heterogeneous effect of peer influence: players with higher tenure and playing levels receive stronger peer influence. Finally, our results suggest that the homophily effect has a stronger influence on players’ behavior than peer influence. Summary of Contribution: The study proposes a novel computational method to separate peer influence from homophily in an online network. Using network embeddings learned from data to control latent homophily, the approach effectively addresses the challenge of correctly identifying peer effects in the absence of randomized experimental conditions. While simplifying the computational process, the method achieves good computational performance, thus effectively helping researchers and practitioners extract useful network information in various online service contexts.