Steiner eccentricity: Predictions and applications - Xingfu Li; Zongbo Yang; Siyi Lin; Jialu Wang (Results in Engineering)
The average Steiner 3-eccentricity, a fundamental graph invariant, has demonstrated efficacy in network similarity assessment and anti-HIV activity prediction. However, due to its computational complexity, exact computation for large graphs remains intractable. To circumvent this challenge, we propose a supervised regression model based on linear Support Vector Machines (SVMs), leveraging graph energy and average eccentricity as pivotal features. The model is trained and tested across a set of synthetic graphs. In practice the model is adopted to predict average Steiner 3-eccentricities of anti-HIV molecules on real-world data which consists of more than 7 thousand molecules. It demonstrates remarkable performance, achieving a mean absolute error (MAE)<0.6 with variance<0.5. These results robustly validate the predictive power of graph energy and average eccentricity as surrogate metrics for approximating the average Steiner 3-eccentricity, thereby offering a scalable solution for practical applications.