Faculty Publications
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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 Analyzing Derived Network Feature Importance to Identify Location Influence in LBSN(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Dewangan, S.K.; Bhattacharjee, S.Location-based social networks (LBSN) data is of greater importance in various domains of studies, such as viral marketing, location recommendation, influence maximization problem, etc. Identifying influential or hotspot locations of disease spread is of great importance during epidemic outbreaks, in case of limited vaccination, to make timely decisions about stay-at-home policy, restricting the transportation/human movement from one geographical location to another, etc. LBSN data can be used to construct the location-based weighted spatio-temporal network with its defined feature sets, including its rich collection of user movement information with respect to space and time. Here, we design two algorithms to generate spatio-temporal edge weights in such networks. We then examine four benchmark algorithms to identify the importance of these derived edge features to evaluate location influence in the context of contagious disease spread. It is observed that deriving edge features play an important role in the application scenario, especially to understand the need of dense network compared to the sparse network, generated from the LBSN data. © 2023 IEEE.