Clique Percolation Research Articles

The Symptom Structure and Causal Relationships of Comorbid Anxiety and Depression Among Chinese Primary and Middle School Teachers: A Network Analysis.

In China, as educational reforms progress, the characteristics of teachers' work have undergone significant changes, resulting in extremely high levels of stress that can trigger anxiety and depression. Anxiety and depression often co-occur, with two mainstream theories explaining this co-existence: the tripartite model and the diathesis-stress model. However, systematic research focusing on this population is relatively scarce, and the applicability of these models has not been thoroughly tested. This study aims to use network analysis methods to examine the interactions between symptoms and analyze the co-existence of anxiety and depression, thereby expanding the research on teachers. Data were provided by the Science Database of People Mental Health, which includes 1670 teachers with a mean age of 30.01. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were used to estimate the network structures of anxiety and depression, respectively. Shared symptoms between depression and anxiety were identified using network analysis and clique percolation methods. Bayesian Networks was used to estimate causal relationships between symptoms. Data were analyzed using R packages. Network structure was constructed with the qgraph package, node centrality and bridge symptoms were evaluated using the networktools package, and network stability was measured via the bootnet package. The Clique Percolation method was implemented with the CliqurPercolation package, and Bayesian network modeling was performed via the Bnlearn package. Dizziness and Easy Fatigability & Weakness were central symptoms in the network. Bridging strength results showed that, the important bridging symptoms included Tachycardia, Depressed Affect, Fatigue, Crying Spell, Easy Fatigability & Weakness, Nightmares, Face Flushing, and Sweating were the strong bridging symptoms. Additionally, Sleep Disturbance played a key mediating role. Depressed Affect and Dissatisfaction were activation symptoms for anxiety-depression co-existence. Using network analysis, this study elucidated core, bridging, and shared symptoms, as well as potential causal pathways between anxiety and depression. Specifically, somatic symptoms are crucial in maintaining and developing the anxiety-depression network among teachers. Sleep disturbance serves as the sole gateway for mild symptoms to develop into other communities. The Bayesian network identified two key activating symptoms within the teacher anxiety-depression network, validating the applicability of the tripartite model among teachers.

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.

Investigating the network structure and causal relationships among bridge symptoms of comorbid depression and anxiety: A Bayesian network analysis.

The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships. Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN. The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions. BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.

A multicomponent psychodynamic treatment for comorbid disorders: a baseline and post-treatment network comparison

ABSTRACTThis study aims to assess pre-post change of 439 patients undergoing a multicomponent treatment (psychodynamic psychotherapy complemented with other treatment components) using a novel network methodology targeting symptoms comorbidity. Patients were recruited from seven clinical sites in the Czech Republic. First, the effectiveness of the treatment was assessed traditionally as a pre-post change in wellbeing, depression, and anxiety using a Bayesian mixed model. The Bayesian factors of time effect (pre-post comparison) on the three outcomes indicate evidence in favor of hypotheses suggesting psychotherapy effectiveness. Second, a network analysis of individual items measuring all three outcomes (Gaussian Graphical Model) was conducted to compare baseline and post-treatment patients’ networks in global edge strength, the topography of the network, the centrality of nodes, and the clique percolation. The network density represented by global edge strength was not affected by the treatment. Nevertheless, the network structure changed in a more qualitative manner into a clearer and separated set of node communities, potentially showing a reduction in comorbidity. The central position of the depression node community in the patients’ self-reported outcome assessment network was replaced by anxiety and wellbeing node communities after treatment.

Federated Clique Percolation for Privacy-preserving Overlapping Community Detection

Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules in biological networks, which make it a hot topic in the field of complex network analysis. With the increasing concerns about the leakage of personal privacy, discovering communities spread across the local networks owned by multiple participants accurately while preserving each participant’s privacy has become an emerging challenge in distributed community detection. In this article, we propose a general federated graph learning model for privacy-preserving distributed graph learning and develop two federated clique percolation algorithms (CPAs) based on it to discover overlapping communities distributed across multiple participants’ local networks without disclosing any participant’s network privacy. Homomorphic encryption and hash operation are used in combination to protect the privacy of the vertices and edges of each local network. Furthermore, vertex attributes are involved in the calculation of clique similarity and clique percolation when dealing with attributed networks. The experimental results on real-world and artificial datasets demonstrate that the proposed algorithms achieve identical results to those of their stand-alone counterparts and more than 200% higher accuracy than the simple distributed CPAs without federating learning.

Network Evolution Model-based prediction of tumor mutation burden from radiomic-clinical features in endometrial cancers

BackgroundEndometrial Cancer (EC) is one of the most prevalent malignancies that affect the female population globally. In the context of immunotherapy, Tumor Mutation Burden (TMB) in the DNA polymerase epsilon (POLE) subtype of this cancer holds promise as a viable therapeutic target.MethodsWe devised a method known as NEM-TIE to forecast the TMB status of patients with endometrial cancer. This approach utilized a combination of the Network Evolution Model, Transfer Information Entropy, Clique Percolation (CP) methodology, and Support Vector Machine (SVM) classification. To construct the Network Evolution Model, we employed an adjacency matrix that utilized transfer information entropy to assess the information gain between nodes of radiomic-clinical features. Subsequently, using the CP algorithm, we unearthed potentially pivotal modules in the Network Evolution Model. Finally, the SVM classifier extracted essential features from the module set.ResultsUpon analyzing the importance of modules, we discovered that the dependence count energy in tumor volumes-of-interest holds immense significance in distinguishing TMB statuses among patients with endometrial cancer. Using the 13 radiomic-clinical features extracted via NEM-TIE, we demonstrated that the area under the receiver operating characteristic curve (AUROC) in the test set is 0.98 (95% confidence interval: 0.95–1.00), surpassing the performance of existing techniques such as the mRMR and Laplacian methods.ConclusionsOur study proposed the NEM-TIE method as a means to identify the TMB status of patients with endometrial cancer. The integration of radiomic-clinical data utilizing the NEM-TIE method may offer a novel technology for supplementary diagnosis.

The application of clique percolation method in health disparities research.

Clique percolation, one of the joint community detection algorithms in network science, is a novel and efficient approach to detecting overlapping communities in real networks. The current study illustrated how clique percolation can help to identify overlapping communities within the complex networks underlying health disparities, particularly highlighting nodes with strong associations with more than one community. A cross-sectional study. The study used a dataset on Latinx populations (N = 1654; mean age = 43.3 years; 53.1% women) as an example to demonstrate the role of such overlapping nodes in the network of syndemic conditions and their common risk factors. Syndemic conditions in the network included HIV risk, substance abuse (smoking, heavy alcohol consumption and marijuana use) and poor mental health. Moreover, the risk factors encompassed individual (education and income) and sociostructural (adverse childhood experiences [ACEs] and access to services) factors. The network was estimated using the R-package bootnet. Clique percolation was conducted on the estimated network using the R-package CliquePercolation. A total of three communities were detected, with HIV risk and poor mental health not being assigned to any community. In general, Community 1 was comprised of ACE categories, Community 2 included education, income and access to services and Community 3 included other syndemic conditions. Of note, two nodes were assigned to two communities: 'household dysfunction' to Communities 1 and 2 and 'smoking' to Communities 2 and 3. Household dysfunction might be the key connector, among other ACEs, to individual and structural barriers. Such barriers further exposed Latinx individuals to risky behaviours, especially smoking, which further linked to marijuana use and heavy alcohol consumption. Clique percolation facilitated our understanding of the complex systems of factors shaping health disparities. The overlapping nodes are promising intervention targets for reducing health disparities in this historically marginalized population. No patient or public contribution.

An exploratory psychometric network analysis of loneliness scales in a sample of older adults

To examine the relationships within and between commonly used measures of loneliness to determine the suitability of the measures in older adults. Further, to determine whether certain items in these measures are more psychometrically robust in terms of capturing different types of loneliness across this population. Data were obtained from 350 older adults via completion of an online survey. Four measures of loneliness were completed. These were the University of California Los Angeles Loneliness Scale 4 (Version 3), the de Jong Gierveld Loneliness Scale, the Social and Emotional Loneliness Scale for Adults (Short Version) and a direct measure of loneliness. Analysis via a regularized partial correlation network and via clique percolation revealed that only the SELSA-S encompassed loneliness relating to deficits in social, family and romantic relationships. The remaining measures tapped mostly into social loneliness alone. The direct measure of loneliness had the strongest connection to the UCLA item-4 and the de Jong Gierveld item-1 exhibited the strongest bridge centrality, being a member of the most clusters. The results indicate that should researchers be interested in assessing loneliness resulting from specific relationships, then the SELSA-S would be the most suitable measure. Whereas the other measures are suitable for assessing loneliness more generally. The results further suggest that the de Jong Gierveld item-1 may be a more suitable direct measure of loneliness than that currently employed as it taps into a greater number of relationships.

Psychological impact of COVID-19: A cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database.

The COVID-19 pandemic and its subsequent health restrictions had an unprecedented impact on mental health, contributing to the emergence and reinforcement of various psychopathological symptoms. This complex interaction needs to be examined especially in a vulnerable population such as older adults. In the present study we analyzed network structures of depressive symptoms, anxiety, and loneliness from the English Longitudinal Study of Aging COVID-19 Substudy over two waves (Months of June-July and November-December 2020). For this purpose, we use measures of centrality (expected and bridge-expected influence) in addition to the Clique Percolation method to identify overlapping symptoms between communities. We also use directed networks to identify direct effects between variables at the longitudinal level. UK adults aged >50 participated, Wave 1: 5,797 (54% female) and Wave 2: 6,512 (56% female). Cross-sectional findings indicated that difficulty relaxing, anxious mood, and excessive worry symptoms were the strongest and similar measures of centrality (Expected Influence) in both waves, while depressive mood was the one that allowed interconnection between all networks (bridge expected influence). On the other hand, sadness and difficulty sleeping were symptoms that reflected the highest comorbidity among all variables during the first and second waves, respectively. Finally, at the longitudinal level, we found a clear predictive effect in the direction of the nervousness symptom, which was reinforced by depressive symptoms (difficulties in enjoying life) and loneliness (feeling of being excluded or cut off from others). Our findings suggest that depressive, anxious, and loneliness symptoms were dynamically reinforced as a function of pandemic context in older adults in the UK.

Data-driven static and dynamic resilience assessment of the global liner shipping network

As a critical infrastructure system of modern society, the global liner shipping network (GLSN) has become increasingly complex and thus vulnerable to disruptions. This study proposes a resilience assessment framework for the GLSN across two dimensions, including static resilience and dynamic resilience. First, by leveraging high-frequency vessel movement data, the GLSN is constructed. Then, with the clique percolation method (CPM), overlapping community structures and key nodes can be identified. The static resilience assessment is initially conducted using simulation techniques, with nodes attacked through pre-designed scenarios. Then, a network disintegration method is employed to consider the impact of traffic flow on system resilience assessment, which separates the weighted GLSN into different layers for evaluation. The results show that both overlapping community structure and traffic flow significantly impact the resilience evaluation of the GLSN. Finally, to assess the dynamic resilience of the GLSN, we propose an innovative, knock-on effect simulation model with tailored, locally weighted flow redistribution rules. It provides a method for predicting the impacts of potential global disruptions (e.g., the COVID-19 pandemic) and critical maritime infrastructure failures (e.g., the Suez Canal obstruction) on the shipping network, which are of great concern not only to academia but also to industry.

CBLA: A Clique Based Louvain Algorithm for Detecting Overlapping Community

Graph mining is one of the significant tasks in the field of computer science. Most of the applications generate a vast amount of data which is represented with the help of a graph. Due to this graph representation, these applications have become complex and increased in size. Finding relevant information from that graph becomes a complicated task. For this community detection algorithms play a vital role in graph partitioning to retrieve relevant information. Finding communities in a graph reduces the complexity of the graph due to related data comes closer to forming a community. Many algorithms have been introduced in the last decade; the Clique percolation method (CPM) is the benchmark algorithm for finding an overlapping community. But in this method, some nodes remain unclassified, nodes that are not part of the clique. Paper proposed the clique-based Louvain algorithm(CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the Louvain algorithm. Louvain algorithm is used to classify the non-overlapped community, but with the help of cliques, it will also detect the overlapped nodes. This paper compared the proposed algorithm with four other benchmark algorithms. The proposed algorithm gives equal or enhanced performance among all compared algorithms.

Geospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India

Wildlife corridors in a landscape include local vegetation, topography, prey base, water and are associated with isolated wildlife habitat patches. They facilitate maintenance of ecological structure and function as well as provide connectivity to faunal populations supporting genetic transfers, and are elements critical to wildlife management. In this work, habitat patches for tiger, both inside as well as outside of Protected Areas have been identified by developing a Habitat Suitability Index model utilizing Remote Sensing and Geographical Information System datasets for the Terai Arc landscape, India. By using a computational approach based on the framework of theory of complex networks, for exclusively pairwise interactions between the habitat patches, a potential tiger corridor network has been structurally identified and studied in this landscape. The interactions between these habitat patches on a spatial scale has been analyzed as a clique of the corridor network. Further, the Clique Percolation Method has been applied to detect overlapping communities of habitat patches in the landscape. The Cliques required for maintaining contiguity between the habitat patches in order to support tiger movement are validated using field observations of tiger communities within the landscape matrix. The model developed for identification of tiger corridors in this study could potentially be of a vital importance for wildlife stakeholders to better understand and manage tiger populations both within and outside of protected areas. The study also highlights Critical Habitat Patches and their importance in maintaining landscape connectivity for tiger dispersal in the landscape. Using a report published by the Government of India as a benchmark, the model presented in the work is found to have an accuracy of 90.73% in predicting tiger carrying patches and the corridor network in the focal landscape.

Comparative Analysis of Overlap Community Detection Techniques on Social Media Platform

Abstract Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users’ implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary’s Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively.

Construction of dynamic protein interaction network based on gene expression data and quartile one principle.

Real protein interaction network (PIN) is dynamic. Researchers created dynamic PIN by combining static PIN with gene expression data to explain the dynamicity evolution of protein interactions. However, all available approaches failed to recognize low- or high-expression proteins as active proteins. Therefore, determining an adequate threshold is one of the system's biological challenges. In this study, a quartile one (q-one) method is proposed to determine the active time points for each protein according to its expression value's features and construct dynamic protein interaction networks (DPINs) in which a protein is added to the network if it is active in two successive time points. This leads to reduce the number of DPINs to half. The efficiency of the q-one approach and three-sigma (3-sigma) method in detecting protein complexes is evaluated using Markov cluster method, clique percolation method, and ClusterONE algorithms. In most cases, q-one outperforms the 3-sigma method in recall, precision and F-measure. This is in addition to its ability to reveal the dynamicity within the protein-protein interaction network and identify essential proteins.

High-Order Community Detection in the Air Transport Industry: A Comparative Analysis among 10 Major International Airlines

Community detection in a complex network is an ongoing field. While the air transport network has gradually formed as a complex system, the topological and geographical characteristics of airline networks have become crucial in understanding the network dynamics and airports’ roles. This research tackles the highly interconnected parts in weighted codeshare networks. A dataset comprising ten major international airlines is selected to conduct a comparative analysis. The result confirms that the clique percolation method can be used in conjunction with other metrics to shed light on air transport network topology, recognizing patterns of inter- and intra-community connections. Moreover, the topological detection results are interpreted and explained from a transport geographical perspective, with the physical airline network structure. As complex as it may seem, the airline network tends to be a relatively small system with only a few high-order communities, which can be characterized by geographical constraints. This research also contributes to the literature by capturing new insights regarding the topological patterns of the air transport industry. Particularly, it reveals the wide hub-shifting phenomenon and the possibility of airlines with different business models sharing an identical topology profile.

MSN‐CDF: New community detection framework to improve routing in mobile social networks

SummaryIn mobile social networks (MSN), portable devices of mobile users communicate with each other. The contact and intercontact times are the two most important parameters that specify the behavior of MSN. Many social features such as similarity, centrality, closeness, and distance are derived from contact and intercontact time patterns. Those parameters and features can be used in the community detection process in community‐based routing as a vector weight of network graph edges. But there is no solution for proper exploitation of them as a multicriteria optimization of community detection algorithms so that the communities become more adaptive with the MSN quiddity and the computational complexity of the algorithms to be stay constant. In this paper, we propose the MSN‐adaptive community detection framework, MSN‐CDF, which meets these demands. We improve the quality function of community detection methods, for example, the Intensity in clique percolation methods and the Modularity in modularity‐based methods, through our new model‐based joint optimization of their quality functions. To this end, to express the MSN contact and social features, we develop a criterion called MCSF. It holds the contact time and intercontact time distributions parameters and implies their social features for MSN purposes. This criterion has as little traffic as possible and enjoys from the probabilistic joint distribution model. The simulations show that MSN‐CDF reduces the network overhead significantly compared with the DiBUBB, Epidemic, and Dynamic Routing. Moreover, it outperforms DiBUBB and Dynamic Routing in terms of delivery rate and delivery latency.

Airline Network Structure: Motifs and Airports’ Role in Cliques

The air transport system can be considered to be a complex network with airports as vertices and direct flights as edges. Research in this area contributes to the optimisation of the airline network and the sustainable development in transportation. This study chose Air China as an example to discover the dynamics of the airline network topologically. Serving as a critical agent of social and economic connections between cities, the airline network structure evolves over time. However, Air China maintains its multicentric and hierarchical structure and forms a mature point-to-point network with codeshare partners. This research also extracts key players at the airport level and investigates the topological structure of highly connected cliques. The results show that the combination of airports in the cliques may be affected by the airline capacity, traffic rights and interline cooperation. Meanwhile, smaller airports appear more often in cliques than hub airports, which can be interpreted and justified with slot limits at mega-airports. The weighted clique percolation method provides new insights to detecting overlapping communities, which can be characterized by geographical constraints. The shared vertices in the combined codeshare network indicate the possible hub shifting in the constantly changing aviation sector.

APAL: Adjacency Propagation Algorithm for overlapping community detection in biological networks

We propose a novel method called Adjacency Propagation Algorithm (APAL) which considers the notion that the adjacent vertices are the best candidates for detecting overlapping communities in an undirected, unweighted, nontrivial graph. This is a compact algorithm with a single threshold parameter used to filter the detected communities according to their intraconnectivity property. In this study, APAL was tested rigorously using synthetic generators, such as the widely accepted LFR benchmark, as well as real data sets of yeast and human protein interactions networks. It was compared against the foremost algorithms in the field; the Clique Percolation Method (CPM), Community Overlap Propagation Algorithm (COPRA) and Neighbourhood-Inflated Seed Expansion (NISE). The results show that APAL outperforms its competitors for networks with increases in the number of memberships of the overlapping vertices. Such conditions are often found in biological networks, where a particular protein subunit may form part of several complexes. We believe that this shows the value of the implementation of APAL for protein interaction and other biological networks.

Secure and smart autonomous multi-robot systems for opinion spammer detection

Reviews of social media have become very important reference indicators for people shopping. However, in order to attract more consumers, some bad merchants organize many fraudulent reviewers to conduct malicious reviews aim at misleading consumers, which is called opinion spammer group. Previous studies have shown that there is an implicit community between reviewers In this paper, we try to use community discovery approach in secure and smart autonomous multi-robot systems for opinion spammer detection, and improve the prediction precision. We propose a novel approach, named SPClique, which is based on the Clique Percolation Method (CPM) to detect opinion spammer groups by modeling the review dataset as reviewer projection graph. During the process, we introduce approximate computing to get a valuable smaller reviewer-projection graph and expand the computing power. It first executes the CPM algorithm in the reviewer-projection graph to find all k-clique clusters, and then innovatively describes an opinion spammer group based on k-clique clusters. Second, the group-based spam and individual-based spams indicators are used to measure the suspiciousness of each opinion spammer group. Finally, it outputs the suspect ranking of opinion spammer groups. The prediction precision of our proposed method is better than four advanced comparison methods, and our approach can detect more real opinion spammers in large-scale review datasets.

Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging.

BackgroundHealth behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.ObjectiveThe aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes.MethodsUsing cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.ResultsBaseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).ConclusionsThis study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.International Registered Report Identifier (IRRID)DERR1-10.2196/24887