Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Shetty, R.D.Subject: search.filters.subject.Graph Neural Network

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    Prediction of Drug Interactions Using Graph-Topological Features and GNN
    (Springer Science and Business Media Deutschland GmbH, 2023) Balamuralidhar, N.; Surendran, P.; Singh, G.; Bhattacharjee, S.; Shetty, R.D.
    The risk of side effects is sometimes inevitable every time two or more drugs are prescribed together, and these side effects of varying adversity levels can be referred to as drug-drug interactions (DDI). Massive amounts of data and the constraints of experimental circumstances result in clinical trials for medication compatibility being time-consuming, risky, expensive, and impractical. Recent research has demonstrated that DDI can be modelled as graphs and experimentally shown that deep learning on graphs can be a practical choice for determining the correlation and side effects of taking multiple medications simultaneously. We propose a novel approach to use inductive graph learning with GraphSAGE, along with topological features, to leverage the structural information of a graph along with the node attributes. An experimental study of the approach is done on a publicly available subset of the DrugBank dataset. We achieve our best results that are comparable with state-of-the-art works using degree, closeness and PageRank centrality measures as additional features with less computational complexity. This study can provide a reliable and cost-effective alternative to clinical trials to predict dangerous side effects, ensuring the safety of patients. © 2023, IFIP International Federation for Information Processing.
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    Does Degree Capture It All? A Case Study of Centrality and Clustering in Signed Networks
    (Association for Computing Machinery, Inc, 2025) Murali, S.S.; Abhin, B.; Shetty, R.D.; Bhattacharjee, S.
    Signed graph networks are used to model systems that contain both positive and negative components. By incorporating signed information into Graph Neural Networks (GNNs), allow for the analysis of complex interactions between nodes, facilitating tasks such as sentiment analysis and trust prediction in social networks. Our main goal in this study is to improve feature selection in a benchmark GNN, Signed Graph Attention (SiGAT) by including centrality and clustering measures other than degree. Our studies reveal that using both degree and centrality features slightly improves signed link prediction performance. Further, our ablation studies revealed that 6 degree features and 16 attention heads optimally encode information and reduce noise. © 2024 Copyright held by the owner/author(s).