Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation

The underlying dynamics of a suicidal ideation latent network model: The role of hopelessness, psychopathology, emotion regulation, and behavioral coping skills in adolescents from the general population

BACKGROUND

Suicide is a major cause of death among adolescents, with suicidal ideation (SI) being a common symptom in this group. SI arises from a complex mix of biological, environmental, and psychological factors, however, the specific relationships between them is not yet fully understood. Network theory has been proposed as a promising framework to analyze these relationships, with latent network models (LNM) offering a novel approach to capture their complex underlying dynamics.

METHODS

We examined a SI-based LNM in a sample of 1539 students from secondary public schools (M = 15.336; SD = 1.022; female = 52.39 %). The model included depressive and anxiety symptoms, feelings of hopelessness, emotion regulation strategies, and cognitive-behavioral and problem-solving skills. Strength and expected influence indices were calculated for each variable.

RESULTS

Hopelessness and depressive symptoms showed the highest strength and expected influence values within the model, respectively. Our findings suggest that hopelessness might play a crucial mediating role linking common mental disorders and emotion regulation strategies with SI in adolescents. Expressive suppression had a direct and negative association with SI, showing its underlying regulatory role when other factors are controlled. Cognitive-behavioral and problem-solving skills showed weak links with SI.

CONCLUSIONS

Primary care- and school-based interventions should center on hopelessness as a relevant direct predictor for SI, and potential mediator in the course of SI. A combination of research and intervention efforts directed at reducing hopelessness in youths may prove to be essential for reducing suicide-related behaviors altogether.

Uncovering unseen ties: a network analysis explores activities of daily living limitations and depression among Chinese older adults

Background

Chinese older adults frequently encounter limitations in activities of daily living (ADL) and experience depression. Prior research has not deeply explored the interconnectedness of these factors through network analysis.

Methods

The study utilized data from 2,137 older adults aged 65 and older, sourced from the 2018 China Health and Retirement Longitudinal Study (CHARLS). The ADL scale and CESD-10 were employed to assess ability to perform ADL and depression, respectively. We conducted network modeling and bridge expected influence (BEI) evaluations to investigate the relationships between these ADL and depression.

Results

Our network analysis revealed robust connections between ADL and depressive symptoms. Specifically, somatic symptoms emerged as significant predictors of depression risk with the highest BEI of 0.21, whereas positive symptoms exhibited a protective effect with the highest BEI of 0.13. Notably, toileting with the highest BEI of 0.04 among the ADL was identified as a pivotal node linking ADL to depression.

Conclusion

This study illuminated the complex interplay between ADL and depression in Chinese older adults, with toileting serving as a crucial connecting point. Our findings offer valuable insights that can inform efforts to enhance mental health and improve the quality of life for this population.

The Interplay Between Sleep and Safety Outcomes in the Workplace: A Scoping Review and Bibliographic Analysis of the Literature

Occupational incidents comprising injuries and accidents remain a serious concern globally. With sleep deprivation and fatigue representing key drivers of many workplace incidents, one strategy to reduce occupational incidents is implementing effective sleep management systems. Yet, to date, there are complaints about the methodological approach in sleep-safety studies. The extent of work carried out with respect to the impact of sleep on safety outcomes needs to be reviewed to highlight the state of the art in the face of increasing technological advancement and changing lifestyle attitudes. A systematic search of the Scopus and PubMed databases retrieved 63 journal articles published up to 2023. The units of analysis included article performance and thematic analysis. It was deduced that workers in healthcare and construction have been the focus of most studies, pointing to the prevalence of safety issues in both these sectors. Most of the studies adopted a quantitative methodology employing validated sleep questionnaires, especially the Pittsburgh Sleep Quality Index. Using thematic analysis, the research focus was mapped into six areas, including sleep disorders, cognition and performance, and injury and accident prevention in the construction sector. In objective studies, alertness and cognitive performance were considered a proxy for sleep deprivation and safety performance. Harmonising sleep questionnaires is necessary to prevent excessive paperwork and ineffective safety systems. This study has the potential to provide occupational health and safety researchers outside of the medicine and psychology disciplines with knowledge on baseline information that could advance efforts to address sleep deprivation and the resulting safety concerns in the workplace.

Differences in emotional expression among college students: a study on integrating psychometric methods and algorithm optimization

BACKGROUND

College students are in an important stage of life development, and their emotional expression ability has a profound impact on their mental health, interpersonal relationships, and academic performance. There are significant differences in emotional expression among individuals, which are influenced by various factors such as gender, cultural background, and personality traits. However, traditional research on emotional expression often relies on a single measurement method, which has problems such as single data dimensions, limited analysis methods, and lack of real-time dynamism and personalization. To overcome these limitations, this study conducted a comprehensive analysis using psychometric methods and algorithm optimization techniques.

METHODS

The Emotional Intelligence Scale (EQ-i) and the depression-anxiety-stress-21 (DASS-21) were used to quantitatively evaluate the emotional state of college students, and their facial expressions and speech emotion data were collected. In order to improve the precision of data analysis, random forests, support vector machines, and neural network machine learning algorithms were applied, and the variance analysis was used to calculate and compare the emotional differences of different genders and academic backgrounds in different grades.

RESULTS

The research results showed that gender, major, and grade differences significantly affected the emotional expression of college students. The F-values for the total EQ-i score of different genders were 7.00, and the F-values for depression, anxiety, and stress scores between different grades were 22.45, 12.48, and 9.14. Male engineering students scored higher in emotional intelligence than female liberal arts students, but liberal arts students showed more significant improvement in later academic years, reflecting the differing impacts of disciplinary environments on emotional development. Female students generally exhibited higher levels of anxiety and stress, particularly those in liberal arts, while female engineering students faced additional psychological burdens due to gender imbalance and biases. Anxiety and stress levels increased across all students as they advanced in their studies, correlating with academic and graduation pressures.

CONCLUSION

This article was based on the integration of psychometric methods and algorithm optimization techniques, exploring the differences in emotional expression among college students, providing new ideas for personalized mental health interventions for college students, enriching the theoretical basis of emotional expression research, and providing important references for education and mental health practice.

Revised network loadings

Psychometric assessment is the foundation of psychological research, where the accuracy of outcomes and their interpretations depend on measurement. Due to the widespread application of factor models, factor loadings are fundamental to modern psychometric assessment. Recent advances in network psychometrics introduced network loadings which aim to provide network models with a metric similar to factor loadings to assess measurement quality when the data are generated from a factor model. Our study revisits and refines the original network loadings to account for properties of (regularized) partial correlation networks, such as the reduction of partial correlation size as the number of variables increase, that were not considered previously. Using a simulation study, the revised network loadings demonstrated greater congruence with the simulated factor loadings across conditions relative to the original formulation. The simulation also evaluated how well correlations between factors can be captured by scores estimated with network loadings. The results show that not only can these network scores adequately estimate the simulated correlations between factors, they can do so without the need for rotation, a standard requirement for factor loadings. The consequence is that researchers do not need to choose a rotation with the revised network loadings, reducing the analytic degrees of freedom and eliminating this common source of variability in factor analysis. We discuss the interpretation of network loadings when data are believed to be generated from a network model and how they may fit into a network theory of measurement.

Revisiting the Structure of the Chinese Version of the Questionnaire of Cognitive and Affective Empathy and Its Relationships with Schizotypy and Paranoia Using Network Approaches

Empathy is predominantly assessed with self-report questionnaires. However, their structural validities were not well-supported. This study aimed to re-explore and refine the factor structure of the Chinese version of the Questionnaire of Cognitive and Affective Empathy (QCAE) and investigate the pathways linked between dimensions of empathy and schizotypy. Data from a valid sample of 1,360 community-dwelling adults (aged 18-35) were subjected to the exploratory graph analysis (EGA) and bootstrap EGA for factor retention. A goodness-of-fit evaluation was conducted using confirmatory factor analysis (CFA). Lastly, a Gaussian graphical model with sum scores of the resultant empathy dimensions, positive, negative, and disorganized schizotypy, and paranoia as nodes was estimated. Results supported a three-factor structure for the revised 20-item QCAE, demonstrating a good model fit. The new Online simulation subscale was associated with reduced disorganized schizotypy, whereas the new Perspective-taking subscale was associated with decreased disorganized schizotypy and increased positive schizotypy. The composite Affective empathy subscale was associated with decreased negative schizotypy and increased positive and disorganized schizotypy and paranoia. Overall, the revised QCAE demonstrated good structural validity, measuring three separable and internally cohesive factors of empathy. Each factor possessed unique and differential relationships with schizotypy dimensions that merit research and clinical attention.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Development and validation of an 8-item version of the Real Relationship Inventory-Client form

OBJECTIVE

To develop and validate a very brief version of the 24-item Real Relationship Inventory-Client (RRI-C) form.

METHOD

Two independent samples of individual psychotherapy patients (Nsample1 = 700, Nsample2 = 434) completed the RRI-C along with other measures. Psychometric scale shortening involved exploratory factor analysis, item response theory analysis, confirmatory factor analysis (CFA), and multigroup CFA. Reliability and convergent and discriminant validity of the scale and subscales were also assessed.

RESULTS

The 8-item RRI-C (RRI-C-SF) preserves the two-factor structure: Genuineness (k = 4, α = .86) and Realism (k = 4, α = .87), which were correlated at r = .74. CFA provided the following fit indices for the bifactor model: X2/df = 2.16, CFI = .99, TLI = .96, RMSEA = .07, and SRMR = .03. Multigroup CFA showed that the RRI-C-SF was invariant across in-person and remote session formats. The RRI-C-SF demonstrated high reliability (α = .91); high correlation with the full-length scale (r = .96); and excellent convergent and discriminant validity with measures of other elements of the therapeutic relationship, personality characteristics, current mental health state, and demographic-clinical variables. Clinical change benchmarks were calculated to serve as valuable tools for both research and clinical practice.

CONCLUSION

The RRI-C-SF is a reliable measure that can be used for both research and clinical purposes. It enables a nuanced assessment of the genuineness and the realism dimensions of the real relationship.

Psychological, psychiatric, and behavioral sciences measurement scales: best practice guidelines for their development and validation

Psychiatric, psychological, and behavioral sciences scales provide quantitative representations of phenomena such as emotions, beliefs, functioning, and social role perceptions. Methodologists and researchers have criticized current scale development practices, emphasizing that inaccurate measurements can derail theory development and clinical decisions, thereby impeding progress in mental health research and practice. These shortcomings often stem from a lack of understanding of appropriate scale development techniques. This article presents a guide to scope, organize, and clarify the process of scale development and validation for psychological and psychiatric use by integrating current methodological literature with the authors' real-world experience. The process is divided into five phases comprising 18 steps. In the Preliminary Phase, the need for a new scale is assessed, including a review of existing measures. In the Item Development Phase, the construct is defined, and an initial pool of items is generated, incorporating literature reviews, expert feedback, and target population evaluation to ensure item relevance and clarity. During the Scale Construction Phase, the scale is finalized through the administration of surveys to a large sample, followed by parallel analysis, exploratory factor, and item descriptive statistics to identify functional items. In the Scale Evaluation Phase, the dimensionality, reliability, and validity of the scale are rigorously tested using both classical and modern psychometric techniques. Finally, in the Finalization Phase, the optimal item sequence is decided, and a comprehensive inventory manual is prepared. In sum, this structured approach provides researchers and clinicians with a comprehensive methodology for developing reliable, valid, and user-friendly psychological, psychiatric, and behavioral sciences measurement scales.

Comorbidity of problematic substance use and other addictive behaviors and anxiety, depression, and post-traumatic stress disorder: a network analysis

BACKGROUND

Among those with common mental health disorders (e.g. mood, anxiety, and stress disorders), comorbidity of substance and other addictive disorders is prevalent. To simplify the seemingly complex relationships underlying such comorbidity, methods that include multiple measures to distill which specific addictions are uniquely associated with specific mental health disorders rather than due to the co-occurrence of other related addictions or mental health disorders can be used.

METHODS

In a general population sample of Jewish adults in Israel (N = 4002), network analysis methods were used to create partial correlation networks of continuous measures of problematic substance (non-medical use of alcohol, tobacco, cannabis, and prescription sedatives, stimulants, and opioid painkillers) and behavioral (gambling, electronic gaming, sexual behavior, pornography, internet, social media, and smartphone) addictions and common mental health problems (depression, anxiety, and post-traumatic stress disorder [PTSD]), adjusted for all variables in the model.

RESULTS

Strongest associations were observed within these clusters: (1) PTSD, anxiety, and depression; (2) problematic substance use and gambling; (3) technology-based addictive behaviors; and (4) problematic sexual behavior and pornography. In terms of comorbidity, the strongest unique associations were observed for PTSD and problematic technology-based behaviors (social media, smartphone), and sedatives and stimulants use; depression and problematic technology-based behaviors (gaming, internet) and sedatives and cannabis use; and anxiety and problematic smartphone use.

CONCLUSIONS

Network analysis isolated unique relationships underlying the observed comorbidity between common mental health problems and addictions, such as associations between mental health problems and technology-based behaviors, which is informative for more focused interventions.

Bayan algorithm: Detecting communities in networks through exact and approximate optimization of modularity

Community detection is a classic network problem with extensive applications in various fields. Its most common method is using modularity maximization heuristics which rarely return an optimal partition or anything similar. Partitions with globally optimal modularity are difficult to compute, and therefore have been underexplored. Using structurally diverse networks, we compare 30 community detection methods including our proposed algorithm that offers optimality and approximation guarantees: the Bayan algorithm. Unlike existing methods, Bayan globally maximizes modularity or approximates it within a factor. Our results show the distinctive accuracy and stability of maximum-modularity partitions in retrieving planted partitions at rates higher than most alternatives for a wide range of parameter settings in two standard benchmarks. Compared to the partitions from 29 other algorithms, maximum-modularity partitions have the best medians for description length, coverage, performance, average conductance, and well clusteredness. These advantages come at the cost of additional computations which Bayan makes possible for small networks (networks that have up to 3000 edges in their largest connected component). Bayan is several times faster than using open-source and commercial solvers for modularity maximization, making it capable of finding optimal partitions for instances that cannot be optimized by any other existing method. Our results point to a few well-performing algorithms, among which Bayan stands out as the most reliable method for small networks. A python implementation of the Bayan algorithm (bayanpy) is publicly available through the package installer for python.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Personal Recovery With Bipolar Disorder: A Network Analysis

BACKGROUND

Personal recovery is valued by people with bipolar disorder (BD), yet its conceptualisation is unclear. Prior work conceptualising personal recovery has focussed on qualitative evidence or clinical factors without considering broader psychosocial factors. This study used a network analysis of Bipolar Recovery Questionnaire (BRQ) responses, aiming to identify (1) independent relationships between items to identify those most "central" to personal recovery and (2) how the relationships between items reflect themes of personal recovery.

METHODS

The model was developed from BRQ responses (36 items) from 394 people diagnosed with bipolar disorder. The undirected network was based on a partial correlation matrix and was weighted. Strength scores were calculated for each node. Community detection analysis identified potential themes. The accuracy of the network was assessed using bootstrapping.

RESULTS

Two consistent communities were identified: "Access to meaningful activity" and "Learning from experiences." "I feel confident enough to get involved in things in life that interest me" was the strongest item, although the strength stability coefficient (0.36) suggested strength should be interpreted with caution. The average edge weight was 0.02; however, stronger edges were identified.

LIMITATIONS

The network showed low stability, possibly due to sample heterogeneity. Future work could incorporate demographic variables, such as time since BD diagnosis or stage of personal recovery, into network estimation.

CONCLUSIONS

Network analysis can be applied to personal recovery, not only clinical symptoms of BD. Clinical applications could include tailoring recovery-focussed therapies towards encouraging important aspects of recovery, such as feeling confident to get involved with life.

Speech-Gesture Matching and Schizotypal Traits: A Network Approach

BACKGROUND AND HYPOTHESIS

Impaired speech-gesture matching has repeatedly been shown in patients with schizophrenia spectrum disorders. Here, we tested the hypothesis that schizotypal traits in the general population are related to reduced speech-gesture matching performance and reduced self-reports about gesture perception. We further explored the relationships between facets of schizotypy and gesture processing in a network model.

STUDY DESIGN

Participants (1094 mainly healthy adults) were presented with concrete or abstract sentences accompanied with videos showing related or unrelated gestures. For each video, participants evaluated the alignment between speech and gesture. They also completed self-rating scales about the perception and production of gestures (Brief Assessment of Gesture scale) and schizotypal traits (Schizotypal Personality Questionnaire-Brief 22-item version). We analyzed bivariate associations and estimated a non-regularized partial Spearman correlation network. We characterized the network by analyzing bridge centrality and controllability metrics of nodes.

STUDY RESULTS

We found a negative relationship between both concrete and abstract gesture-speech matching performance and overall schizotypy. In the network, disorganization had the highest average controllability and it was negatively related to abstract speech-gesture matching. Bridge centralities indicated that self-reported production of gestures to enhance communication in social interactions connects self-reported gesture perception, schizotypal traits, and gesture processing task performance.

CONCLUSION

The association between impaired abstract speech-gesture matching and disorganization supports a continuum between schizophrenia and schizotypy. Using gestures to facilitate communication connects subjective and objective aspects of gesture processing and schizotypal traits. Future interventional studies in patients should test the potential causal pathways implied by this network model.

New Psychometric Evidence of the Grief Impairment Scale (GIS) in People Who Have Experienced the Death of a Loved One From a Network Psychometric Approach in Two Latin American Countries

This study aimed to evaluate the psychometric properties of the Grief Impairment Scale (GIS) using a network psychometric model. A total of 1048 individuals from Peru and El Salvador participated. A network psychometric model was used to determine internal structure, reliability, and cross-country invariance. The results indicate that the GIS items were grouped into a single network structure through Exploratory Graph Analysis. Reliability was estimated by structural consistency, and it was found that when replicating the network structure within an empirical dimension, a single network structure was consistently obtained, and all items remained stable. Furthermore, the network structure was invariant, thus functioning similarly across the different country groups. In conclusion, the GIS presented solid psychometric evidence of validity based on its internal structure, reliability, and cross-country invariance. Therefore, the GIS is a psychometrically sound measure of functional impairment symptoms due to grief for Peruvian and Salvadoran individuals.

Burnout and depression in college students

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

Improving the Walktrap Algorithm Using K-Means Clustering

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

Aesthetic emotions are affected by context: a psychometric network analysis

Aesthetic emotions are defined as emotions arising when a person evaluates a stimulus for its aesthetic appeal. Whether these emotions are unique to aesthetic activities is debated. We address this debate by examining if recollections of different types of engaging activities entail different emotional profiles. A large sample of participants were asked to recall engaging aesthetic (N = 167), non-aesthetic (N = 160), or consumer (N = 172) activities. They rated the extent to which 75 candidate aesthetic emotions were evoked by these activities. We applied a computational psychometric network approach to represent and compare the space of these emotions across the three conditions. At the behavioral level, recalled aesthetic activities were rated as the least vivid but most intense compared to the two other conditions. At the network level, we found several quantitative differences across the three conditions, related to the typology, community (clusters) and core nodes (emotions) of these networks. Our results suggest that aesthetic and non-aesthetic activities evoke emotional spaces differently. Thus, we propose that aesthetic emotions are distributed differently in a multidimensional aesthetic space than for other engaging activities. Our results highlight the context-specificity of aesthetic emotions.