Latent Interaction Research Articles

Pitman–Yor process mixture model for community structure exploration considering latent interaction patterns* *Project supported by Beijing Natural Science Foundation, China (Grant Nos. L181010 and 4172054), the National Key R&D Program of China (GrantNo. 2016YFB0801100), and the National Basic Research Program of China (Grant No. 2013CB329605).

The statistical model for community detection is a promising research area in network analysis. Most existing statistical models of community detection are designed for networks with a known type of community structure, but in many practical situations, the types of community structures are unknown. To cope with unknown community structures, diverse types should be considered in one model. We propose a model that incorporates the latent interaction pattern, which is regarded as the basis of constructions of diverse community structures by us. The interaction pattern can parameterize various types of community structures in one model. A collapsed Gibbs sampling inference is proposed to estimate the community assignments and other hyper-parameters. With the Pitman–Yor process as a prior, our model can automatically detect the numbers and sizes of communities without a known type of community structure beforehand. Via Bayesian inference, our model can detect some hidden interaction patterns that offer extra information for network analysis. Experiments on networks with diverse community structures demonstrate that our model outperforms four state-of-the-art models.

Family Processes, Parenting Practices, and Psychosocial Maturity of Chinese Youths: A Latent Variable Interaction and Mediation Analysis.

Development of psychosocial maturity has profound implications for youths’ well-being and positive development in the long run. Nevertheless, little research has investigated the way family socialization contributes to youths’ psychosocial maturity. Both the concepts of family socialization and psychosocial maturity are multifaceted and latent, which may lead to biased results if studied by manifest variables. Also, no existing research has discovered how different family socialization components interact latently to contribute to youths’ psychosocial maturity. The current study, based on a sample of 533 Chinese parent-youth dyads, examined the effects of family socialization by positive family processes and authoritative parenting, and their latent interaction in an integrated moderation and mediation modeling framework on Chinese youths’ psychosocial maturity. Results showed that both positive family processes and authoritative parenting, and their latent interaction significantly predicted the higher psychosocial maturity of Chinese youths. Authoritative parenting acted as a mediator for the relationship between positive family processes and Chinese youths’ psychosocial maturity. Furthermore, the mediating effect of authoritative parenting was conditioned by different contexts of positive family processes, the strongest and least strong effects found in high and low positive family processes, respectively, and moderate effect observed in medium positive family processes. Findings of the current study contribute to our understanding of the complicated family mechanism in relation to youth development, especially in this digital era.

R-squared of a Latent Interaction in Structural Equation Model: A Tutorial of Using R

Mplus (Muthén & Muthén, 1998 - 2017) is one popular statistical software to estimate the latent interaction effects using the latent moderated structural equation approach (LMS). However, the variance explained by a latent interaction that supports the interpretation of estimation results is not currently available from the Mplus output. To relieve human computations and to facilitate interpretations of latent interaction effects in social science research, we developed two functions (LIR & LOIR) in the R package IRmplus to calculate the R-squared of a latent interaction above and beyond the first-order simple main effects in Structural Equation Modeling. This tutorial provides a step-by-step guide for applied researchers to estimating a latent interaction effect in Mplus, and to obtaining the R-squared of a latent interaction effect using the LIR & LOIR functions. Example data and syntax are available online.

An Observational Study on Cephalometric Characteristics and Patterns Associated with the Prader–Willi Syndrome: A Structural Equation Modelling and Network Approach

Examining specific patterns of major cranio-facial alterations through cephalometric measurements in order to improve the Prader–Willi (PWS) syndrome diagnostic poses a major challenge of identifying interlinkages between numerous credentials. These interactions can be captured through probabilistic models of conditional independence between heterogeneous variables. Our research included 18 subjects (aged 4 to 28 years) genetically diagnosed with Prader–Willi syndrome and a healthy control group (matched age and sex). A morphometric and cephalometric analysis was performed upon all the subjects in order to obtain the needed specific data. We have, therefore, firstly deployed several integrated Gaussian graphical models (GGMs) to capture the positive and negative partial correlations and the intensity of the connections between numerous credentials configured to determine specific cranio-facial characteristics of patients with PWS compared to others without this genetic disorder (case-control analysis). Afterwards, we applied structural equation modelling (SEM) with latent class analysis to assess the impact of these coordinates on the prevalence of the Prader–Willi diagnostic. We found that there are latent interactions of features affected by external variables, and the interlinkages are strapping particularly between cranial base (with an important role in craniofacial disharmonies) and facial heights, as important characteristic patterns in determining the Prader–Willi diagnostic, while the overall patterns are significantly different in PWS and the control group. These results impact the field by providing an enhanced comprehensive perspective on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome that can be used as benchmarks in determining the diagnostic of this rare genetic disorder. Furthermore, the two innovative exploratory research tools applied in this paper are very useful to the craniofacial field to infer the connections/dependencies between variables (particularly biological variables and genes) on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome.

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program

IntroductionAlthough various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and...

Predicting lncRNA–miRNA interactions based on interactome network and graphlet interaction

In the development and treatment of many human diseases, the regulatory roles between lncRNAs and miRNAs are important, but much remains unknown about them; moreover, experimental methods for analyzing them are expensive and time-consuming. In this work, we applied a semi-supervised interactome network-based approach to explore and forecast the latent interaction between lncRNAs and miRNAs. We constructed graphs according to the similarity of each of lncRNAs and miRNAs and determined the number of graphlet interaction isomers between nodes in these two graphs. According to the two graphs and the known interactive relationship, we calculated a score for lncRNA–miRNA pairs, as the prediction result. The results showed that the model (LMI-INGI) was reliable in fivefold cross-validation (AUC = 0.8957, PRE = 0.6815, REC = 0.8842, F1 score = 0.7452, AUPR = 0.9213). We also tested the model with data based on the similarity of expression profile and similarity of function for verifying the applicability of LMI-INGI, and the resulting AUC value was 0.9197 and 0.9006, respectively. Compared with the other four algorithms and variable similarity tests, our model successfully demonstrated superiority and good generalizability. LMI-INGI would be helpful in forecasting interactions between lncRNAs and miRNAs.

Testing Moderation in Business and Psychological Studies with Latent Moderated Structural Equations

Most organizational researchers understand the detrimental effects of measurement errors in testing relationships among latent variables and hence adopt structural equation modeling (SEM) to control for measurement errors. Nonetheless, many of them revert to regression-based approaches, such as moderated multiple regression (MMR), when testing for moderating and other nonlinear effects. The predominance of MMR is likely due to the limited evidence showing the superiority of latent interaction approaches over regression-based approaches combined with the previous complicated procedures for testing latent interactions. In this teaching note, we first briefly explain the latent moderated structural equations (LMS) approach, which estimates latent interaction effects while controlling for measurement errors. Then we explain the reliability-corrected single-indicator LMS (RCSLMS) approach to testing latent interactions with summated scales and correcting for measurement errors, yielding results which approximate those from LMS. Next, we report simulation results illustrating that LMS and RCSLMS outperform MMR in terms of accuracy of point estimates and confidence intervals for interaction effects under various conditions. Then, we show how LMS and RCSLMS can be implemented with Mplus, providing an example-based tutorial to demonstrate a 4-step procedure for testing a range of latent interactions, as well as the decisions at each step. Finally, we conclude with answers to some frequently asked questions when testing latent interactions. As supplementary files to support researchers, we provide a narrated PowerPoint presentation, all Mplus syntax and output files, data sets for numerical examples, and Excel files for conducting the loglikelihood values difference test and plotting the latent interaction effects.

Robust Detection of Malicious URLs with Self-Paced Wide & Deep Learning

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. It is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they can not provide robust performance due to the diversity of cybercrimes and can not cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction.

Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources' latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.

Simple view of reading among Slovenian English foreign language learners: A latent interaction modeling approach

An estimated 700 million people learn how to read in English as a foreign language (EFL). This highlights the need to better understand the components influencing EFL reading. We tested to what degree EFL decoding and linguistic comprehension influenced EFL reading comprehension within the realm of three variants of the Simple View of Reading (SVR) model – the multiplicative, the additive, and the combined variants. Moreover, we estimated the extent to which first language reading contributed to EFL reading comprehension over and above EFL components. Participants were 690 Slovenian seventh graders (Mage = 12.76 years; SD = 0.43). Utilizing a latent interaction model, results supported the SVR additive variant. EFL decoding was a stronger contributor than EFL linguistic comprehension. First language reading did not seem to influence EFL reading comprehension. Our findings suggest that EFL instruction should focus on practicing EFL decoding and linguistic comprehension independently.

Latent Logistic Interaction Modeling: A Simulation and Empirical Illustration of Type D Personality

ABSTRACT This study focuses on three popular methods to model interactions between two constructs containing measurement error in predicting an observed binary outcome: logistic regression using (1) observed scores, (2) factor scores, and (3) Structural Equation Modeling (SEM). It is still unclear how they compare with respect to bias and precision in the estimated interaction when item scores underlying the interaction constructs are skewed and ordinal. In this article, we investigated this issue using both a Monte Carlo simulation and an empirical illustration of the effect of Type D personality on cardiac events. Our results indicated that the logistic regression using SEM performed best in terms of bias and confidence interval coverage, especially at sample sizes of 500 or larger. Although for most methods bias increased when item scores were skewed and ordinal, SEM produced relatively unbiased interaction effect estimates when items were modeled as ordered categorical.

Racially Motivated Hate Crimes and Incidents Against Immigrants in Lithuania

The article analyses the experiences of racially motivated hate incidents against immigrants in Lithuania, based on the data of a qualitative study. Interviews with informants in the study show that prejudice, verbal and physical violence incidents are an integral part of the daily lives of immigrants with visible ethnic and racial identities. Racist hate incidents make it possible to identify latent interactions between immigrants and local residents. Based on examples of hate incidents, the article critically analyses the typology of hate crimes by Levin and McDevitt (1993) and the limitations of its application in hate incident analysis.

Exploring the Performance of Latent Moderated Structural Equations Approach for Ordered-Categorical Items

ABSTRACT Latent moderated structural equations (LMS) is a popular method in estimating latent interaction effects. Mplus has implemented a variant of LMS (LMS-cat) that uses categorical confirmatory factor analysis to handle ordered-categorical indicators. We conducted a simulation study to examine the performance of the LMS-cat in varying sample size, interaction effect size, missing data rate and scenario, as well as number and symmetry of item response category conditions. Results showed that the LMS-cat is an excellent method in estimating structural parameters (i.e., interaction effect and lower-order effects). However, it could produce highly biased measurement parameters (i.e., factor loadings, and item category thresholds). We illustrated the LMS-cat by testing the interaction effect between participation in teachers` support activities and that of teachers` counterfactual activities (meditation, meditative movement, vigorous physical exercise) on teachers` sense of self-efficacy.

Meaningful Work Protects Teachers’ Self-Rated Health under Stressors

A sense of meaningfulness is one of the most sought?after work characteristics which has been associated with employees’ well-being. This study explored whether meaningful work enhances self-rated health in challenging work context, under the stressors of distractions, unnecessary tasks, and unreasonable tasks. Data was collected from Finnish teachers (N = 1,658) and structural equation modelling was employed with the latent interaction terms. Results showed that meaningful work was associated with better self-rated health and the stressors were associated with poorer self-rated health. Protective potential of meaningful work against stressors was also discovered, as meaningful work mitigated the harm of stressors on self-rated health. These findings indicate that meaningful work acts as an important resource for employees’ self-rated health and helps them to better cope with stressful work conditions, enhancing well-being. The protective quality of meaningful work means that even challenging work context may have less harm for employees’ well-being, if they have a strong sense of meaning in work. The practical implications of the findings for teachers and organizations are discussed.

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions.

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

GSSA: Pay attention to graph feature importance for GCN via statistical self-attention

Graph convolutional network (GCN) has been proved to be an effective framework for graph-based semi-supervised learning applications. The core operation block of GCN is the convolutional layer, which enables the network to construct node embeddings by fusing both attributes of nodes and relationships between nodes. Different features or feature interactions inherently have various influences on the convolutional layers. However, there are very limited studies about the impact of feature importance in GCN-related communities. In this work, we attempt to augment convolutional layers in GCNs with statistical attention-based feature importance by modeling the latent interactions of features, which is complementary to the standard GCNs and only needs simple calculations with statistics rather than heavy trainings. To this end, we treat the feature input of each convolutional layer as a separate multi-layer heterogeneous graph, and propose Graph Statistical Self-Attention (GSSA) method to automatically learn the hierarchical structure of feature importance. More specifically, we propose two modules in GSSA, Channel-wise Self-Attention (CSA) to capture the dependencies between feature channels, and Mean-based Self-Attention (MSA) to reweight similarities among features. Aiming at each graph convolutional layer, GSSA can be applied in a ”plug and play” way for a wide range of GCN variants. To the best of our knowledge, this is the first implementation that optimizes GCNs from the feature importance perspective. Extensive experiments demonstrate that GSSA can promote existing popular baselines remarkably in semi-supervised node classification tasks. We further employ multiple qualitative evaluations to get deep insights into our method.

Violation of Distributional Assumptions in Latent Interaction Models

Violating the distributional assumptions of latent interaction models can lead to biased estimates and invalid inference. However, no statistical procedure is readily available to verify whether these assumptions hold. We develop several specification tests contrasting consistent latent interaction estimators—which are robust to violations of distributional assumptions (i.e., Extended Unconstrained Indicator Approach and Model-Implied Instrumental Variables method)—to an efficient estimator (i.e., Latent Moderated Structural Equations), which is inconsistent under non-normally distributed linear latent variables. We compare these estimators under a variety of conditions. The robust Hausman test we propose works well in identifying misspecifications due to violations of distributional assumptions of the latent variables. Moreover, our results indicate that the Latent Moderated Structural Equations method is severely bias under non-ideal conditions. Thus, it should not be used as the default approach and its results should always be compared to consistent estimators that are robust to distributional assumptions.

Moderating Effect of Family Support on the Mediated Relation Between Negative Life Events and Antisocial Behavior Tendencies via Self-Esteem Among Chinese Adolescents.

Adolescents are particularly prone to antisocial behavior. The promoting effect of negative life events on antisocial behavior has been well-documented. However, the internal influence mechanisms between negative life events and antisocial behavior tendencies in adolescents are still unknown. The aim of this study was to explore the mediation effect of self-esteem and the moderated mediation effect of family support between negative life events and antisocial behavior tendencies in 8,958 adolescents who were selected from three Chinese provinces. Robust maximum likelihood estimator (MLR) of a structural equation model (SEM) was applied to test the mediation model and the moderated mediation model. The results revealed that negative life events had a positive effect on antisocial behavior tendencies in adolescents, with a direct effect of 0.082 (95% CI: 0.052, 0.111) and an indirect effect via self-esteem of 0.168 (95% CI: 0.146, 0.191). Negative life events had a 67.20% effect on antisocial behavior tendencies, where self-esteem showed mediation. The indirect effect was 2.049-fold greater than the direct effect. Furthermore, the effect of latent interaction of subjective family support and negative life events on self-esteem was negatively significant (β = −0.018, p = 0.032, 95% CI: −0.035, −0.002). The indirect effect of negative life events was greater, where subjective family support was below 1 SD of the mean (conditional indirect effect = 0.227, 95% CI = 0.200, 0.255) than where it was above 1 SD of the mean (conditional indirect effect = 0.203, 95% CI = 0.177, 0.229). The moderated mediation effect index was −0.012, p = 0.033. Moderated mediation showed that the mediated path was less evident in the students who had greater subjective support from family. The results of the current study demonstrated the important role that self-esteem and subjective family support played in minimizing the adverse effect of negative life events on antisocial behavior development of adolescents. These findings have important implications for preventing antisocial behavior in adolescents by developing interventions aimed at enhancing their self-esteem and providing support-skill training to parents aimed at improving subjective family support of adolescents.

Interrelationship Between Inquiry-Based Learning and Instructional Quality in Predicting Science Literacy

Recent international comparison studies such as the Programme for International Student Assessment (PISA) repeatedly argued that inquiry-based learning (IBL) indicated a negative effect on science literacy (SL). However, those studies included two limitations in revealing the relationship between IBL and SL as they did not consider (1) instructional quality and (2) different types of IBL. Accordingly, this study aimed to explore relationships between two types of IBL (open and guided) and SL, and how four types of instructional quality (classroom management, adaptative teaching, teacher–student relationship, and teacher support) moderated the relationships between IBL and SL. For this purpose, a PISA 2015 Finnish sample was used and analyzed by latent moderated structural equation modeling in order to explore the latent interaction between IBL and the quality of instruction. The results of this study presented that the teacher–student relationship was the most potent predictor of SL and moderated the effects of guided and open inquiry on SL. Classroom management also indicated moderation effects for both guided and open inquiry. Also, this study showed the overruling effect of open inquiry on guided inquiry in predicting SL.

Control-value appraisals, achievement emotions, and foreign language performance: A latent interaction analysis

Based on control-value theory (CVT), this study (N = 550 Chinese university students) examined relations between control-value appraisals, subsequent achievement emotions, and resulting performance in foreign language (FL) learning. The results show that perceived control and value related positively to positive emotions (enjoyment, hope, pride) and FL performance, and negatively to negative emotions (anger, anxiety, shame, hopelessness, boredom). Control and value interacted in predicting all eight emotions and FL performance. The multiplicative impact of the appraisals on performance was mediated by four of the focal emotions. These findings elucidate the impact of appraisals and emotions on achievement and support the generalizability of CVT to foreign language learning. Directions for future research and implications for education are discussed.