Community Detection
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PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260624
Author(s):  
Hadi Sam Nariman ◽  
Lan Anh Nguyen Luu ◽  
Márton Hadarics

Using the 9th round of European Social Survey (ESS), we explored the relationship between Europeans’ basic values and their attitudes towards immigrants. Employing a latent class analysis (LCA), we classified the respondents based on three items capturing the extent to which participants would support allowing three groups of immigrants to enter and live in their countries: immigrants of same ethnic groups, immigrants of different ethnic groups, and immigrants from poorer countries outside Europe. Four classes of Europeans with mutually exclusive response patterns with respect to their inclusive attitudes towards immigrants were found. The classes were named Inclusive (highly inclusive), Some (selective), Few (highly selective), and Exclusive (highly exclusive). Next, using a network technique, a partial correlation network of 10 basic human values was estimated for each class of participants. The four networks were compared to each other based on three network properties namely: global connectivity, community detection, and assortativity coefficient. The global connectivity (the overall level of interconnections) between the 10 basic values was found to be mostly invariant across the four networks. However, results of the community detection analysis revealed a more complex value structure among the most inclusive class of Europeans. Further, according to the assortativity analysis, as expected, for the most inclusive Europeans, values with similar motivational backgrounds were found to be interconnected most strongly to one another. We further discussed the theoretical and practical implications of our findings.


2021 ◽  
Author(s):  
M. Ángeles Serrano ◽  
Marián Boguñá

Real networks comprise from hundreds to millions of interacting elements and permeate all contexts, from technology to biology to society. All of them display non-trivial connectivity patterns, including the small-world phenomenon, making nodes to be separated by a small number of intermediate links. As a consequence, networks present an apparent lack of metric structure and are difficult to map. Yet, many networks have a hidden geometry that enables meaningful maps in the two-dimensional hyperbolic plane. The discovery of such hidden geometry and the understanding of its role have become fundamental questions in network science giving rise to the field of network geometry. This Element reviews fundamental models and methods for the geometric description of real networks with a focus on applications of real network maps, including decentralized routing protocols, geometric community detection, and the self-similar multiscale unfolding of networks by geometric renormalization.


2021 ◽  
Author(s):  
Ali Osman Berk Sapci ◽  
Shan Lu ◽  
Oznur Tastan ◽  
Sunduz Keles

Developments in single-cell RNA sequencing (scRNA-seq) advanced our understanding of transcriptional programs of different cell types and cellular stages at the individual cell level. Single-cell RNA-seq datasets across multiple individuals and time points are now routinely generated for different conditions. Analysis of personalized dynamic gene networks constructed from these datasets could unravel subject-specific network-level variation critical for phenotypic differences. While there have been developments in the gene module discovery methods on networks estimated from scRNA-seq data, these have mostly focused on static gene networks. In this work, we develop MuDCoD to cluster genes in personalized dynamic gene networks and identify gene modules that vary not only across time but also among subjects. To this end, MuDCoD extends the global spectral clustering framework of the previously developed method, PisCES, to promote information sharing among the subject as well as the time domain. Our computational experiments across a wide variety of settings indicate that, when present, MuDCoD leverages shared signals among networks of the subjects, and performs robustly when subjects do not share any apparent information. An application to human-induced pluripotent stem cell scRNA-seq data during dopaminergic neuron differentiation indicates that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid the exploration of subject-specific biological processes that vary across time.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lunzhong Zhang ◽  
Shu Han ◽  
Manli Zhao ◽  
Runshun Zhang ◽  
Xuebin Zhang ◽  
...  

Background and Objectives. The development of network medicine provides new opportunities for disease research. Ischemic stroke has a high incidence, disability, and recurrence rate, and one of the reasons is that it is often accompanied by other complex diseases, including risk factors, complications, and comorbidities. Network medicine was used to try to analyze the characteristics of IS-related diseases and find out the differences in genetic pathways between Chinese herbs and Western drugs. Methods. Individualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed. Finally, genetic data of symptoms, herbs, and drugs were used for pathway and GO analysis to explore the characteristics of pathways of subgroups and to compare the similarities and differences in genetic pathways of herbs and drugs from the perspective of molecular pathways of symptoms. Results. We identified 34 patient modules from the PSN, of which 7 modules include 98.48% of the whole cases. The 7 patient subgroups have their own characteristics of risk factors, complications, and comorbidities and the underlying genetic pathways of symptoms, drugs, and herbs. Each subgroup has the largest number of herb pathways. For specific symptom pathways, the number of herb pathways is more than that of drugs. Conclusion. The research of disease classification based on community detection of symptom-shared patient networks is practical; the common molecular pathway of symptoms and herbs reflects the rationality of TCM herbs on symptoms and the wide range of therapeutic targets.


2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Olha Buchel ◽  
Anton Ninkov ◽  
Danise Cathel ◽  
Yaneer Bar-Yam ◽  
Leila Hedayatifar

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.


2021 ◽  
Vol 12 ◽  
Author(s):  
Tulsi A. Radhoe ◽  
Joost A. Agelink van Rentergem ◽  
Almar A. L. Kok ◽  
Martijn Huisman ◽  
Hilde M. Geurts

Objectives: In this study, we aim to discover whether there are valid subgroups in aging that are defined by modifiable factors and are determinant of clinically relevant outcomes regarding healthy aging.Method: Data from interviews were collected in the Longitudinal Aging Study Amsterdam at two measurement occasions with a 3-year interval. Input for the analyses were seven well-known vulnerability and protective factors of healthy aging. By means of community detection, we tested whether we could distinguish subgroups in a sample of 1478 participants (T1-sample, aged 61–101 years). We tested both the external validity (T1) and predictive validity (T2) for wellbeing and subjective cognitive decline. Moreover, replicability and long-term stability were determined in 1186 participants (T2-sample, aged 61–101 years).Results: Three similar subgroups were identified at T1 and T2. Subgroup A was characterized by high levels of education with personal vulnerabilities, subgroup B by being physically active with low support and low levels of education, and subgroup C by high levels of support with low levels of education. Subgroup C showed the lowest wellbeing and memory profile, both at T1 and T2. On most measures of wellbeing and memory, subgroups A and B did not differ from each other. At T2, the same number of subgroups was identified and subgroup profiles at T1 and T2 were practically identical. Per T1 subgroup 47–62% retained their membership at T2.Discussion: We identified valid subgroups that replicate over time and differ on external variables at current and later measurement occasions. Individual change in subgroup membership over time shows that transitions to subgroups with better outcomes are possible.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Leonardo Morelli ◽  
Valentina Giansanti ◽  
Davide Cittaro

AbstractSingle cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, , that is compatible with the popular framework.


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260201
Author(s):  
Seyed Arman Haghbayan ◽  
Nikolas Geroliminis ◽  
Meisam Akbarzadeh

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.


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