首页|VIT-AP University Researchers Update Knowledge of Machine Learning (Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures)
VIT-AP University Researchers Update Knowledge of Machine Learning (Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures)
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NETL
NSTL
IEEE
Investigators discuss new findings in artificial intelligence. According to news reporting originating from Andhra Pradesh, India, by NewsRx correspondents, research stated, “The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes.” Financial supporters for this research include National Science And Technology Council, Taiwan; Chang Gung Memorial Hospital, Taoyuan, Taiwan. Our news journalists obtained a quote from the research from VIT-AP University: “To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node’s true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios.”
NETL
NSTL
IEEE
