Figure - uploaded by Víthor Rosa Franco
Content may be subject to copyright.
Source publication
Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graph in which the properties of the reduced graph are to be induced from the properties of the larger original graph. This paper introduces both a new method for reducing chain graphs to simpler directed acyclic graphs (DAGs), that we call power chain graph...
Context in source publication
Context 1
... = data generating process; PA-EFA = parallel analysis with exploratory factor analysis; EGA = exploratory graph analysis; CGMM = correlation Gaussian mixture model; GARI = graph adjusted Rand index; VI = variation of information; NMI = normalized mutual information; HitND = simulation rate of hits of number of clusters. Table 5 shows the effect of the sample size (n, the number of simulated cases) on the per formance of the clustering procedures. Again, CGMM had the best performance over all conditions and over all indices. ...Similar publications
Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. Howev...
The research on node classification is based on node embeddings. Node classification accuracy can be improved if the embeddings of different nodes are well discriminated. With the rapid development of deep learning, researchers have proposed many graph neural network models (GNNs), such as GCN and GAT, which generally obtain node embeddings by aggr...
We study a natural combinatorial pricing problem for sequentially arriving buyers with equal budgets. Each buyer is interested in exactly one pair of items and purchases this pair if and only if, upon arrival, both items are still available and the sum of the item prices does not exceed the budget. The goal of the seller is to set prices to the ite...
Unitary coined discrete-time quantum walks (UCDTQW) constitute a universal model of computation, meaning that any computation done by a general purpose quantum computer can either be done using the UCDTQW framework. In the last decades, great progress has been made in this field by developing quantum walk-based algorithms that can outperform classi...
Following the success of Word2Vec embeddings, graph embeddings (GEs) have gained substantial traction. GEs are commonly generated and evaluated extrinsically on downstream applications, but intrinsic evaluations of the original graph properties in terms of topological structure and semantic information have been lacking. Understanding these will he...
Citations
The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PAPCA) or principal axis, and exploratory graph analysis with Louvain clustering (EGALV). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PAPCA (PAPCA-FS) and EGALV (EGALV-FS). Additionally, we examined the direct multilevel solution provided by EGALV. All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGALV and PAPCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAPCA-FS and EGALV-FS showed a close to perfect accuracy across all the conditions, while EGALV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGALV (group factors) and EGALV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
