Multilayer Community Detection Research Articles

Delineating Neighborhoods: An Approach Combining Urban Morphology with Point and Flow Datasets

Although neighborhoods are a widely used analytical concept in urban geography, they are often proxied using grids or statistical sectors in empirical research. The rationales underlying these proxies are often separated from the theoretical considerations of what makes a neighborhood a neighborhood, casting shadows over their relevance and applicability. In this article, we identify two specific challenges separating empirical operationalizations from theoretical considerations in neighborhood delineations: (1) not incorporating key built environment elements and (2) monodimensional approaches. We develop a method that addresses this double challenge by (1) creating morphological basic spatial units (BSUs) and (2) aggregating them into neighborhoods using multilayer community detection (MLCD) drawing on datasets used in both formal and functional regionalization approaches. We illustrate this method for the case of Leuven, Belgium, by (1) using street blocks as BSUs and (2) focusing on proximity, land use, and social interactions. Through a comparative analysis, we show that our results align with theoretical considerations and perform as well as, and perhaps better, than statistical sectors and grids as neighborhood representations. We therefore argue that this flexible method can bridge formal and functional regionalization approaches making the case for its adoption in neighborhood delineation exercises.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Gene communities in co-expression networks across different tissues.

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.

Evaluating Community Detection Algorithms for Multilayer Networks: Effectiveness of Link Weights and Link Direction

Analyzing the structures of multilayer networks (MLNs) has been a hot research topic in network science. Community detection algorithms are important tools for analyzing MLNs. In the literature, several community detection algorithms for MLNs have been proposed. Moreover, there are several options for the graph representation of an MLN: for example, directed or undirected, weighted or unweighted, and using information from all or only some layers. Although these options may affect the results of community detection in MLNs, representations that are effective for community detection have not yet been clarified. In this paper, we experimentally evaluate the effectiveness of three types of community detection algorithms for MLNs and examine how the graph representation of an MLN affects the results of these algorithms. Our main findings are as follows: (1) The flattening approach is particularly effective, whereas the layer-by-layer approach is not applicable to detecting communities in MLNs of Twitter users. (2) Using a directed graph for each layer of an MLN increases the accuracy of community detection. (3) The Leiden method, which is a community detection algorithm for single-layer networks, achieves comparable accuracy with the community detection algorithms for MLNs, which suggests that there exists room for improvement in multilayer community detection algorithms for effectively utilizing the multilayer structures of MLNs.

A Unified Spectral Clustering Approach for Detecting Community Structure in Multilayer Networks

Networks offer a compact representation of complex systems such as social, communication, and biological systems. Traditional network models are often inadequate to capture the diverse nature of contemporary networks, which may exhibit temporal variation and multiple types of interactions between entities. Multilayer networks (MLNs) provide a more comprehensive representation by allowing interactions between nodes to be represented by different types of links, each reflecting a distinct type of interaction. Community detection reveals meaningful structure and provides a better understanding of the overall functioning of networks. Current approaches to multilayer community detection are either limited to community detection over the aggregated network or are extensions of single-layer community detection methods with simplifying assumptions such as a common community structure across layers. Moreover, most of the existing methods are limited to multiplex networks with no inter-layer edges. In this paper, we introduce a spectral-clustering-based community detection method for two-layer MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the within- and across-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods. MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the intra- and inter-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods.

Community detection in multi-frequency EEG networks

Functional connectivity networks of the human brain are commonly studied using tools from complex network theory. Existing methods focus on functional connectivity within a single frequency band. However, it is well-known that higher order brain functions rely on the integration of information across oscillations at different frequencies. Therefore, there is a need to study these cross-frequency interactions. In this paper, we use multilayer networks to model functional connectivity across multiple frequencies, where each layer corresponds to a different frequency band. We then introduce the multilayer modularity metric to develop a multilayer community detection algorithm. The proposed approach is applied to electroencephalogram (EEG) data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed following the correct response.

Increased flexibility of brain dynamics in patients with multiple sclerosis.

Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.

Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy.

Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis. Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network. Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms. Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.

Decoding the heterogeneity of Alzheimer’s disease diagnosis and progression using multilayer networks

Alzheimer’s disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.

Multi-layer analysis of multi-frequency brain networks as a new tool to study EEG topological organization.

Oscillatory activity rising from the interaction among neurons is widely observed in the brain at different scales and is thought to encode distinctive properties of the neural processing. Classical investigations of neuroelectrical activity and connectivity usually focus on specific frequency bands, considered as separate aspects of brain functioning. However, this might not paint the whole picture, preventing to see the brain activity as a whole, as the result of an integrated process. This study aims to provide a new framework for the analysis of the functional interaction between brain regions across frequencies and different subjects. We ground our work on the latest advances in graph theory, exploiting multi-layer community detection. In our multi-layer network model, layers keep track of single frequencies, including all the information in a unique graph. Community detection is then applied by means of a multilayer formulation of modularity. As a proof-of-concept of our approach, we provide here an application to multi-frequency functional brain networks derived from resting state EEG collected in a group of healthy subjects. Our results indicate that α-band selectively characterizes an inter-individual common organization of EEG brain networks during open eyes resting state. Future applications of this new approach may include the extraction of subject-specific features able to capture selected properties of the brain processes, related to physiological or pathological conditions.

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks.

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.

Identification of multi-layer networks community by fusing nonnegative matrix factorization and topological structural information

The accumulated multi-layer networks in nature and society provide a great opportunity for revealing the mechanisms of the underlying complex systems with multiple types of interactions. Community detection in multi-layer networks aims to extract well-connected groups of vertices for all layers, which shed light into revealing the structure–function relations. The current algorithms either exploit the topological structure of multi-layer networks or explore the latent features of networks, which are criticized due to their low accuracy because they ignore the relation among various layers. To attack these problems, a novel algorithm for Multi-layer community detection by using joint Nonnegative Matrix Factorization (MjNMF) is proposed, which simultaneously considers the topological structure and relations of layers. Specifically, MjNMF extracts features of vertices for each layer by simultaneously factorizing the adjacency matrices of all layers with a common basis matrix, where features of vertices preserve the topological structure of all layers. To obtain community structure, MjNMF decomposes the similarity matrices of vertices for all layers in concern. The smoothness strategy is adopted to connect features of various layers with community structure by learning a project matrix for each layer. Finally, MjNMF integrates feature extraction, community detection, and smoothness by formulating an overall objective function, and derives the optimization rules. The experimental results on ten multi-layer networks demonstrate the proposed algorithm significantly outperforms thirteen state-of-the-art methods in terms of various measurements.

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test–retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test–retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test–retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed – a process likely to be limited for most due to the lack of test–retest samples to enable parameter optimization.

Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling

The generalized version of modularity for multilayer networks, a.k.a. multislice modularity, is characterized by two model parameters, namely resolution factor and inter-layer coupling factor. The former corresponds to a notion of layer-specific relevance, whereas the inter-layer coupling factor represents the strength of node connections across the network layers. Despite the potential of this approach, the setting of both parameters can be arbitrarily selected, without considering specific characteristics from the topology of the multilayer network as well as from an available community structure. Also, the multislice modularity is not designed to explicitly model order relations over the layers, which is of prior importance for dynamic networks. This paper aims to overcome the main limitations of the multislice modularity by introducing a new formulation of modularity for multilayer networks. We revise the role and semantics of both the resolution and inter-layer coupling factors based on information available from the within-layer and inter-layer structures of the multilayer communities. Also, our proposed multilayer modularity is general enough to consider orderings of network layers and their constraints on layer coupling. Experiments were carried out on synthetic and real-world multilayer networks using state-of-the-art approaches for multilayer community detection. The obtained results have shown the meaningfulness of the proposed modularity, revealing the effects of different combinations of the resolution and inter-layer coupling functions. This work also represents a starting point for the development of new optimization methods for community detection in multilayer networks.

Higher-Order Multi-Layer Community Detection

In this paper, we define a new problem of multi-layer network community detection, namely higher-order multi-layer community detection. A multi-layer motif (M-Motif) approach is proposed, which discovers communities with good intralayer higher-order community quality while preserving interlayer higher-order community consistency. Experimental results have confirmed the superiority of the proposed method.

The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks.

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.

Multilayer overlapping community detection using multi-objective optimization

Abstract Community detection in multilayer networks such as social or information networks, due to its vast practical applications has attracted many attentions in the past years. Although some researches have been done to develop monoplex methods to multilayers, but because of the complexity of multilayer networks they are in their infancy. In this study, initially, the definition of community in single layer networks is extended and a new definition for multilayer community is presented. Then, regarding the importance of overlapping communities in real networks, a comprehensive definition for overlapping multilayer community is presented. Furthermore, in order to address the problem of multilayer community detection, a two-phase approach has been adopted. In the first phase, a multi-objective mathematical model is developed, and optimized using a genetic algorithm based method (NSGA-II) to achieve a set of solutions (Pareto front). Each potential solution represents a partition of the multilayer network. In the second phase, the best solution is chosen from the Pareto fronts, using a novel algorithm. This proposed algorithm, handles the obstacles of genetic representation and is able to detect the optimal multilayer overlapping communities. Experiments on both synthetic mLFR networks with different parameters and six real data networks show the performance of the algorithm in terms of NMI and multilayer modularity.

Optimizing state change detection in functional temporal networks through dynamic community detection

Abstract Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However, modularity maximization, a popular method for performing multilayer community detection, requires the specification of an appropriate null network as well as resolution and interlayer coupling parameters. Importantly, the ability of the algorithm to accurately detect community evolution is dependent on the choice of these parameters. In functional temporal networks, where evolving communities reflect changing functional relationships between network nodes, it is especially important that the detected communities reflect any state changes of the system. Here, we present analytical work suggesting that a uniform null network provides improved sensitivity to the detection of small evolving communities in temporal networks with positive edge weights bounded above by 1, such as certain types of correlation networks. We then propose a method for increasing the sensitivity of modularity maximization to state changes in nodal dynamics by modelling self-identity links between layers based on the self-similarity of the network nodes between layers. This method is more appropriate for functional temporal networks from both a modelling and mathematical perspective, as it incorporates the dynamic nature of network nodes. We motivate our method based on applications in neuroscience where network nodes represent neurons and functional edges represent similarity of firing patterns in time. We show that in simulated data sets of neuronal spike trains, updating interlayer links based on the firing properties of the neurons provides superior community detection of evolving network structure when groups of neurons change their firing properties over time. Finally, we apply our method to experimental calcium imaging data that monitors the spiking activity of hundreds of neurons to track the evolution of neuronal communities during a state change from the awake to anaesthetized state.

Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI.

In recent years, resting state fMRI has been widely utilized to understand the functional organization of the brain for healthy and disease populations. Recent studies show that functional connectivity during resting state is a dynamic process. Studying this temporal dynamics provides a better understanding of the human brain compared to static network analysis. In this paper, a new tensor based temporal and multi-layer community detection algorithm is introduced to identify and track the brain network community structure across time and subjects. The framework studies the temporal evolution of communities in fMRI connectivity networks constructed across different regions of interests. The proposed approach relies on determining the subspace that best describes the community structure using Tucker decomposition of the tensor. The brain dynamics are summarized into a set of functional connectivity states that are repeated over time and subjects. The dynamic behavior of the brain is evaluated in terms of consistency of different subnetworks during resting state. The results illustrate that some of the networks, such as the default mode, cognitive control and bilateral limbic networks, have low consistency over time indicating their dynamic behavior. The results indicate that the functional connectivity of the brain is dynamic and the detected community structure experiences smooth temporal evolution. The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.