The laser ablation ⁴⁰Ar/³⁹Ar method makes it possible to date quanterary volcanic rocks precisely. So far, it lacks an assessment criterion that can be used to estimate the reliability of test data and geochronological information. The mean square weighted deviation (MSWD) and the number of measurement (n value) are used to be the crucial parameter to evaluate sample properties and experimental standard. When applying laser ablation ⁴⁰Ar/³⁹Ar method to date young volcanic rocks, a best-fit ⁴⁰Ar/³⁹Ar isochron diagram requires isotopic homogeneity of the sample and permanent closure of the isotope system at the same time. While MSWD, which is related to n value, is a parameter to determine whether the data are located in the same normal distribution, or in other words, whether the sample has a unified true age, it is applicable to use MSWD and n value to estimate the level of confidence placed on the computed mean. The critical values of MSWD for the 99.7% confidence interval using differing degrees of freedom are calculated in this paper. It is important to note that MSWD is available in evaluating dating result of both apparent and isochron ages. For the samples which tend to have heterogeneous initial ⁴⁰Ar/³⁶Ar ratios or different closure times, MSWD combined with the probability density of apparent ages can be used to distinguish different groups of samples, calculate the isochron ages respectively, and interpret the result after accounting for the various parameters to evaluate the data quality.

In response to the inadequacies of current graph compression methods, such as higher time complexity, dependence on experiences to set parameters, too many parameters to adjust, compression loss, ignoring the community structure of network, a social network compression method is proposed based on the importance of the community nodes. The method include community discovery algorithm (GS) based on greedy strategy and social network compression algorithm (SNC). Adopting topological potential theory GS algorithm is not only capable of discovering communities but also capable of mining important nodes in the communities. SNC algorithm takes communities as targets, achieves lossless compression while maintaining the connections between communities, and keeps important nodes in communities or basic community structure if necessary. The feasibility and effectiveness of the method are verified in experiments.