A SYSTEMATIC INVESTIGATION OF WITHIN-SUBJECT AND BETWEEN-SUBJECT COVARIANCE STRUCTURES IN GROWTH MIXTURE MODELS

The current study investigated how between-subject and within-subject variance-covariance structures affected the detection of a finite mixture of unobserved subpopulations and parameter recovery of growth mixture models in the context of linear mixed-effects models. A simulation study was conducted to evaluate the impact of variance-covariance structure difference, mean separation, mixture proportion and sample size on parameter estimates from growth mixture models. Data were generated based on 2-class growth mixture model framework and estimated by 1-, 2-, and 3-class growth mixture models using Mplus. Bias, precision and efficiency of parameter estimates were assessed as well as the model enumeration accuracy and classification quality. Results suggested that sample size and data overlap were key factors influencing the convergence rates and possibilities of local maxima in the estimation of GMM models. BIC outperformed ABIC and LMR in identifying the correct number of latent classes. Model enumeration using BIC could be improved by increasing sample size and/or decreasing overall data overlap, and the latter had more impact. Relative bias of parameters was smaller when subpopulation data were more separated. Both the magnitude of mean and variance-covariance separation and variance-covariance differences impacted parameter recovery. Across all conditions, parameter recovery was better for intercept and slope estimates than variance and covariances estimates. Entropy values were as high as the acceptable standards suggested by previous studies for any of the conditions even when data were very well-separated. Class membership assignment was more accurate when mean growth trajectories were more different among subpopulations and mixing proportions were more balanced

Empirical analysis of the ship-transport network of China

Structural properties of the ship-transport network of China (STNC) are studied in the light of recent investigations of complex networks. STNC is composed of a set of routes and ports located along the sea or river. Network properties including the degree distribution, degree correlations, clustering, shortest path length, centrality and betweenness are studied in different definition of network topology. It is found that geographical constraint plays an important role in the network topology of STNC. We also study the traffic flow of STNC based on the weighted network representation, and demonstrate the weight distribution can be described by power law or exponential function depending on the assumed definition of network topology. Other features related to STNC are also investigated.Comment: 20 pages, 7 figures, 1 tabl

A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study

A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification

A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study

A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification

Graph Augmentation Clustering Network

Existing graph clustering networks heavily rely on a predefined graph and may fail if the initial graph is of low quality. To tackle this issue, we propose a novel graph augmentation clustering network capable of adaptively enhancing the initial graph to achieve better clustering performance. Specifically, we first integrate the node attribute and topology structure information to learn the latent feature representation. Then, we explore the local geometric structure information on the embedding space to construct an adjacency graph and subsequently develop an adaptive graph augmentation architecture to fuse that graph with the initial one dynamically. Finally, we minimize the Jeffreys divergence between multiple derived distributions to conduct network training in an unsupervised fashion. Extensive experiments on six commonly used benchmark datasets demonstrate that the proposed method consistently outperforms several state-of-the-art approaches. In particular, our method improves the ARI by more than 9.39\% over the best baseline on DBLP. The source codes and data have been submitted to the appendix