Faculty Publications
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Publications by NITK Faculty
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Item GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.; Dutta, A.; Namtirtha, A.The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's τ improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic. © 2014 IEEE.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.Item NDWC: Global Scaling With Reducing Factor for Influence Ranking in Weighted Complex Networks(Institute of Electrical and Electronics Engineers Inc., 2025) Shetty, R.D.; Manoj, T.; Bhattacharjee, S.; Vasudeva, n.Optimal nodes identification in weighted complex networks is an essential task across diverse areas such as epidemiology, social media, infrastructure, and information diffusion. Traditional centrality measures often fail to capture the nuanced influence of a node when edge weights vary significantly across the network. In the scope of this study, propose a novel centrality measure, Normalized Degree and Weight Centrality (NDWC), that incorporates global scaling and a reducing factor to better assess the importance of nodes in weighted networks. NDWC integrates both structural (degree-based) and strength-based (edge weight) contributions, normalized using global standard deviations to ensure fair comparisons. Furthermore, a reducing factor is introduced to penalize nodes with skewed edge weight distributions, enhancing robustness against local heterogeneity. By combining these elements, NDWC provides a more balanced and representative ranking of nodes. Experimental validation on widely used datasets demonstrates that NDWC outperforms several state-of-the-art methods in identifying influential nodes, particularly in weighted networks. © 2013 IEEE.