Faculty Publications
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Publications by NITK Faculty
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Item A Temporal Metric-Based Efficient Approach to Predict Citation Counts of Scientists(Springer Science and Business Media Deutschland GmbH, 2023) Dewangan, S.K.; Bhattacharjee, S.; Shetty, R.D.Citation count is one of the essential factors in understanding and measuring the impact of a scientist or a publication. Estimating the future impact of scientists or publications is crucial as it assists in making decisions about potential awardees of research grants, appointing researchers for several scientific positions, etc. Many studies have been proposed to estimate publication’s future citation count; however, limited research has been conducted on forecasting the citation-based influence of the scientists. The authors of the scientific manuscripts are connected through common publications, which can be captured in dynamic network structures with multiple features in the nodes and the links. The topological structure is an essential factor to consider as it reveals important information about such dynamic networks, such as the rise and fall in the network properties like in-degree, etc., over time for nodes. In this work, we have developed an approach for predicting the citation count of scientists using topological information from dynamic citation networks and relevant contents of individual publications. This framework of the citation count prediction is formulated as the node classification task, which is accomplished by using seven machine learning-based classification models for various class categories. The highest average accuracy of 85.19% is achieved with the XGBoost classifier on the High Energy Physics - Theory citation network dataset. © 2023, IFIP International Federation for Information Processing.Item Node Classification in Weighted Complex Networks Using Neighborhood Feature Similarity(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, R.D.; Bhattacharjee, S.; Thanmai, K.The potential of graph representation learning schemes has attained great acceptance in diverse, complex network applications. Most of the existing Graph Neural Network (GNN) architectures explore the node features aggregation and feature transformation within the neighborhoods, mainly performed on the unweighted graphs. Also, the existing GNN architectures consider all sets of neighborhood features, which are computationally expensive tasks. Practically, most of the real-world graphs are weighted graphs, and it is important to learn the representation of weighted graphs. In this work, we generate and leverage information of the best possible feature combination from the multiple levels of the networks. Edge weights and the connection structure are considered for generating node embedding, and classifying the node more accurately. The proposed framework, Similarity Feature Embedding GNN (SFEGNN), can be efficiently used for node classification in the weighted networks by leveraging feature overlap similarity from the network structure. This novel approach is helpful in modeling weighted networks for node classification and determining how strongly the neighborhood features are correlated. We validate the efficacy of SFEGNN on six benchmark datasets with varying degrees of homophily ratio and found that it is effective even for highly heterophily networks. Our model has empirically outperformed the state-of-the-art GNN framework with the highest accuracy improvement of 28.88%. © 2017 IEEE.