A Mini review of Node Centrality Metrics in Biological Networks

The diversity of nodes in a complex network causes each node to have varying significance, and the important nodes often have a significant impact on the structure and function of the network. Although the interpretation of the results of biological networks must always depend on the topological study of nodes, there is presently no consensus on how to use these metrics, and most network analyses always result in a basic interpretation of a limited number of metrics. To thoroughly comprehend biological networks, it is necessary to consistently understand the notion of node centrality. Therefore, for 10 typical nodal metrics in biological networks, the study first assesses their current applications, advantages, disadvantages as well as potential applications. Then, a review of previous studies is provided, and suggestions are made correspondingly for the purpose of improving biological topology algorithms. Finally, the following recommendations are made in this study: (1) a comprehensive and accurate assessment of node centrality necessitates the use of multiple metrics, including both the target node and its surroundings, and density of maximum neighbourhood component(DMNC) can be used as a complement to other node centrality metrics; (2) different centrality metrics can be applied to identify nodes with different functions, which in this study are mapped as modular surroundings, bridging roles, and susceptibility; and (3) the following groups of node centrality can often be verified against each other, including degree and maximum neighbourhood component (MNC), eccentricity, closeness and radiality; stress and betweenness

A simple feature extraction method for estimating the whole life cycle state of health of lithium-ion batteries using transformer-based neural network

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) can avoid safety accidents and economic losses, and it remains a big research challenge. In this paper, electrochemical impedance spectroscopy (EIS) is used as the feature for the SOH prediction. EIS contains rich information such as material properties and electrochemical reactions, which directly reflects the aging state of LIBs. In order to obtain valuable data for SOH estimation, we propose a new feature extraction method from the perspective of electrochemistry, and then apply the transformer-based neural network for SOH estimation. Through feature extraction, the mean absolute percentage error of the estimation is reduced to 1.63% in the whole life cycle, which is decreased by 70% compared to the original data before feature extraction