Degree Distribution

Degree distribution analysis in networks focuses on understanding the frequency of different node connection counts (degrees), aiming to characterize network structure and inform model development. Current research emphasizes improved inference methods for accurately identifying power-law distributions and addressing biases in parameter estimation, particularly using Bayesian approaches. Furthermore, studies investigate the impact of imbalanced degree distributions (long-tailed distributions) on graph neural network (GNN) performance, leading to the development of normalization techniques and expert model architectures to mitigate these effects. These advancements are crucial for improving the accuracy and reliability of network analysis and GNN applications across various domains.