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.

Robo or unsolicited calls have become a persistent issue in telecommunication networks, posing significant challenges to individuals, businesses, and regulatory authorities. These calls not only trick users into disclosing their private and financial information, but also affect their productivity through unwanted phone ringing. A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential harm. Therein, this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a network. The trust score of the callers is then used along with machine learning models to classify them as legitimate or robo-caller. We use a large anonymized dataset (call detailed records) from a large telecommunication provider containing more than 1 billion records collected over 10 days. We have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error rate. Specifically, the proposed features when used collectively achieve a true-positive rate of around 97% with a false-positive rate of less than 0.01%.