Attributed graphs have an additional sign vector for each node. Typically, edge signs represent like or dislike relationship between the node pairs. This has applications in domains, such as recommender systems, personalised search, etc. However, limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap. Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes. The study of attributed networks is challenging due to noise, sparsity, and class imbalance issues. In this work, we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks. We propose Semi-supervised Node Classification in Attributed graphs (SNCA). SNCA is robust to underlying network noise, and has in-built class imbalance handling capabilities. We perform an extensive experimental study on real-world datasets to showcase the efficiency, scalability, robustness, and pertinence of the solution. The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.