This article reviews computational approaches for predicting protein function using protein interaction networks. Functional annotation of proteins is crucial in the post-genomic era, and the availability of protein interaction data has led to the development of various methods. The article outlines two main categories of approaches: direct methods and module-assisted methods. Direct methods infer protein function based on network connections, while module-assisted methods identify functional modules within the network and use them for annotation. The article discusses various techniques, including neighborhood counting, graph-theoretic methods, Markov random fields, and integration of multiple data sources. It also highlights the importance of evaluating these methods systematically and making them accessible to the biological community. The review emphasizes the need for further research and the potential of network-based approaches in improving functional annotation.This article reviews computational approaches for predicting protein function using protein interaction networks. Functional annotation of proteins is crucial in the post-genomic era, and the availability of protein interaction data has led to the development of various methods. The article outlines two main categories of approaches: direct methods and module-assisted methods. Direct methods infer protein function based on network connections, while module-assisted methods identify functional modules within the network and use them for annotation. The article discusses various techniques, including neighborhood counting, graph-theoretic methods, Markov random fields, and integration of multiple data sources. It also highlights the importance of evaluating these methods systematically and making them accessible to the biological community. The review emphasizes the need for further research and the potential of network-based approaches in improving functional annotation.