Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Subject: search.filters.subject.Weighted networks

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    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.
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    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.