Louvain Modularity Research Articles

Envisioning the Future of Mobility: A Well-Being-Oriented Approach

Mobility, a vital part of daily life, significantly impacts human well-being. Understanding this relationship is crucial for shaping the future trajectory of mobility, a connection often overlooked in previous research. This study explores the complex relationship between mobility and well-being and proposes a holistic framework for mobility’s future, prioritizing individual and societal well-being. The motivation for this research stems from the growing need to balance technological advancements in transportation with the well-being of diverse populations, especially as the mobility landscape evolves with innovations like autonomous vehicles and intelligent mobility solutions. We employ bibliometric methods, analyzing 53,588 academic articles to identify key themes and research trends related to mobility and well-being. This study categorizes these articles into thematic clusters using the Louvain modularity maximization algorithm, which facilitates the formation of cohesive groups based on citation patterns. Our findings underline the significant impact of mobility on physical, mental, psychological, financial, and social well-being. The proposed framework features four pillars: vehicle, infrastructure and environment, mobility stakeholders, and policy. This framework underscores the importance of collaboration between institutional and individual actions in shaping a future mobility landscape that is technologically advanced, socially responsible, and conducive to an improved quality of life.

Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach

A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

Graph clustering with Boltzmann machines

Graph clustering is the process of labeling nodes so that nodes sharing common labels form densely connected subgraphs with sparser connections to the remaining vertices. Because of its difficult formulation, we translate the intra-cluster density maximization problem to a distance minimization problem. To achieve this reformulation, we use a novel vertex–vertex distance that accurately reflects density. Specifically, we extend the recent binary quadratic K-medoids formulation to graph clustering. We also generalize a quadratic formulation originally designed for partitioning complete graphs. Because binary quadratic optimization is an NP-hard problem, we obtain numerical solutions for these formulations through the use of two novel Boltzmann machine (meta-)heuristics. For benchmarking purposes, we compare solution quality and computational performances to those obtained using a commercial solver, Gurobi. We also compare clustering quality to the clusters obtained using the popular Louvain modularity maximization method. Our initial results clearly demonstrate the superiority of our problem formulations combined with our Boltzmann machines. In the case of smaller less complex graphs, our formulations solved using Boltzmann machines provide the same solutions as Gurobi, but with solution times that are orders of magnitude lower. In the case of larger and more complex graphs, Gurobi either fails to return meaningful results within a reasonable time frame or returns inferior results. Finally, we also note that both our clustering formulations, the distance minimization and K-medoids, when solved using our Boltzmann machines, yield clusters of superior quality to those obtained with the Louvain algorithm.

Public Opinions about Palliative and End-of-life Care during the COVID-19 Pandemic: A Twitter-based Study.

Palliative and end-of-life care (PEoLC) played a critical role in relieving suffering and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinion concerning the PEoLC during the pandemic. Given that social media has the potential to collect real-time public opinions, analysis of this evidence is vital to guide future policymaking. To use social media data to investigate real-time public opinions toward PEoLC during the COVID-19 crisis, and explore the impact of the vaccine programme on public opinions about PEoLC. A Twitter-based study exploring tweets across three English-speaking countries - the United States, United Kingdom and Canada. From October 2020 to March 2021, 7,951 PEoLC-related tweets with geographical tags were retrieved and identified from a large-scale COVID-19 Twitter data set through Twitter API (Application Programming Interface). Topic modelling realized through a PMI-based co-occurrence network and Louvain Modularity was utilized to examine latent topics across countries and across two time periods (pre- and post-vaccination periods). Commonalities and regional differences between PEoLC topics in the United States, United Kingdom and Canada were identified specifically: (i) Cancer care and care facilities were of common interest to the public across three countries during the pandemic; (ii) The public expressed positive attitudes towards the vaccine and highlighted its protection to PEoLC professionals; (iii) Whilst Twitter users shared their personal experiences about PEoLC in the online community during the pandemic this was more prominent in the United States and Canada. The implementation of the vaccine program raised the profile of the vaccine discussion, however, this did not influence public opinions about PEoLC. Public opinions on Twitter reflected a need for enhanced Palliative and End-of-Life Care (PEoLC) services during the COVID-19 pandemic. The insignificant impact of the vaccine programme on public discussion on social media indicated that public concerns toward PEoLC continued to persist even after vaccination efforts. Insights gleaned from PEoLC public opinions could provide some clues for policymakers on how to ensure high-quality PEoLC during public emergencies. In this post-COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from online public discussion to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media's potential in acting as an effective tool to reflect public opinions in the context of PEoLC.

Exploring economic integration of peasant settlements in Roman Central Spain (1st c. − 3rd c. AD)

Preventive excavations in the metropolitan area of Madrid (Spain) resulted in the study of the archaeological record of a number of Roman non-elite rural sites, which uniquely allow for the analysis of ancient peasant economies in central Roman Spain. We engage with current debates on the economic roles and integration of peasant sites in the Roman imperial economy, and report on the application of a network science method to the economic networks established among these settlements. The Louvain modularity measure is used to explore two competing hypotheses on the level of integration or segregation of peasant communities within the Roman economy. The results suggest a pattern of weak integration that was maintained with little change between the 1st and the 3rd century CE.

Expanding Louvain Algorithm for Clustering Relationship Formation

Community detection is a method to determine and to discover the existence of cluster or group that share the same interest, hobbies, purposes, projects, lifestyles, location or profession. There are some example of community detection algorithms that have been developed, such as strongly connected components algorithm, weakly connected components, label propagation, triangle count and average clustering coefficient, spectral optimization, Newman and Louvain modularity algorithm. Louvain method is the most efficient algorithm to detect communities in large scale network. Expansion of the Louvain Algorithm is carried out by forming a community based on connections between nodes (users) which are developed by adding weights to nodes to form clusters or referred to as clustering relationships. The next step is to perform weighting based on user relationships using a weighting algorithm that is formed by considering user account activity, such as giving each other recommendation comments, or to decide whether the relationship between the followers and the following is exist or not. The results of this study are the best modularity created with a value of 0.879 and the cluster test is 0.776.

Topological data analysis identified prognostically-distinct phenotypes in MitraClip patients

Abstract Background Topological data analysis (TDA) is the state-of-the-art unsupervised machine learning framework that can provide insight into the dataset and visualize condensed information via the topological network graph. This robust approach was never used to assess the heterogeneous MitraClip population. Purpose We aim to develop a TDA model that will identify prognostically-distinct phenogroups in MitraClip patients without a priori knowledge of the population and their outcomes. Method Patients who underwent MitraClip (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database for baseline and follow-up data. Thirteen variables were used for TDA. The topological network graph was created using the Python Scikit-TDA Kepler-Mapper package (v. 2.0.1), and clustering was performed at the graph level with Louvain's modularity method. Kaplan-Meier survival analysis was used to assess the all-cause mortality endpoint of each cluster identified in an unsupervised manner. The dataset with cluster labels was also used to train a Light Gradient Boosted Machine model, and SHapley Additive exPlanations (SHAP) analysis was applied to determine the feature importance. Results A total of 389 consecutive patients were included in the final analysis and two major clusters consisting of 384 patients were identified. The mean age was 80.3±8.7 years; 256 (65.8%) were male. The mean Society of Thoracic Surgeons Mitral Valve Replacement risk score was 9.6±6.9%. Fifty-five (14.5%) patients died during the mean follow duration (185 days). Kaplan-Meier analysis showed significant survival differences among the two clusters (HR: 2.38; 95% CI: 1.39–4.06, p=0.001; Figure 1). Clusters 1 (n=195) was associated with > mild residual mitral regurgitation and worse survival performance and was characterized with worse tricuspid regurgitation severity, a higher proportion of patients with atrial fibrillation/flutter, anterior leaflet prolapse, and mitral annular/leaflet calcification, as summarized in Table 1. Conclusion TDA can identify distinct phenotype clusters with prognostic significance in MitraClip patients based on mitral valve morphology and clinical risk factors. This simple model can facilitate clinical risk stratification for MitraClip patients regarding procedural success and survival performance. Funding Acknowledgement Type of funding sources: None.

SCNIC: Sparse correlation network investigation for compositional data.

Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. To address these challenges, we developed SCNIC: Sparse Cooccurrence Network Investigation for compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.

The brainstem connectome database

Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.

Application of Natural Language Processing to Learn Insights on the Clinician's Lived Experience of Electronic Health Records.

We interviewed six clinicians to learn about their lived experience using electronic health records (EHR, Allscripts users) using a semi-structured interview guide in an academic medical center in New York City from October to November 2016. Each participant interview lasted approximately one to two hours. We applied a clustering algorithm to the interview transcript to detect topics, applying natural language processing (NLP). We visualized eight themes using network diagrams (Louvain modularity 0.70). Novel findings include the need for a concise and organized display and data entry page, the user controlling functions for orders, medications, radiology reports, and missing signals of indentation or filtering functions in the order page and lab results. Application of topic modeling to qualitative interview data provides far-reaching research insights into the clinicians' lived experience of EHR and future optimal EHR design to address human-computer interaction issues in an acute care setting.

Urban mobility and resilience: exploring Boston\u2019s urban mobility network through twitter data

Human mobility connects urban dwellers and neighborhoods and impacts social equity. An in-depth understanding of human mobility helps to enhance urban resilience. However, limited research has focused on mobility resilience. Building on previous research, this study looks at the neighborhood connectivity enabled by urban mobility. We analyze the aggregated mobility patterns in Boston through the coupling of network structure and social characteristics. Geocoded twitter data combined with socioeconomic datasets were used to create a mobility-based urban network. Through the quantitative analysis, we found that the social segregation in Boston shapes its mobility network. Network communities identified by the Louvain modularity algorithm are often self-containing, meaning that their residents are more likely to move within their communities. A multinomial regression reveals that spatial racial and income segregation has a strong impact on the dynamic segregation of the network. The beneficial network characteristics –e.g. higher density and well-connected motifs– are less present in areas with bolder presence of minorities. Thus, the resilience state is not equitable among neighborhoods of different income levels and races, indicating that the resilience measures of urban networks need to be adapted according to sociodemographic characteristics.

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

Climate risks, digital media, and big data: following communication trails to investigate urban communities' resilience

Abstract. Today, when extreme weather affects an urban area, huge numbers of digital data are spontaneously produced by the population on the Internet. These “digital trails” can provide insight into the interactions existing between climate-related risks and the social perception of these risks. According to this research “big data” exploration techniques can be exploited to monitor these interactions and their effect on urban resilience. The experiments presented in this paper show that digital research can amplify key issues covered by digital media and identify the stakeholders that can influence the debate, and therefore the community's attitudes towards an issue. Three corpora of Web communication data have been extracted: press articles covering the June 2016 Seine River flood, press articles covering the October 2015 Alpes-Maritimes flood, and tweets on the 2016 Seine River flood. The analysis of these datasets involved an iteration between manual and automated extraction of hundreds of key terms, aggregated analysis of publication incidence and key term incidence, graph representations based on measures of semantic proximity (conditional distance) between key terms, automated visualisation of clusters through Louvain modularity, visual observation of the graph, and quantitative analysis of its nodes and edges. Through this analysis we detected topics and actors that characterise each press dataset, as well as frequent co-occurrences and clusters of topics and actors. Profiling of social media users gave us insights into who could influence opinions on Twitter. Through a comparison of the three datasets, it was also possible to observe how some patterns change over time, in different urban areas and in different digital media contexts.

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

BackgroundTwitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks.ObjectivesThis study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California.MethodsFrom a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities.ResultsThe modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles.ConclusionsThe most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.

Analysis and visualization of social user communities

In this paper, a novel framework for social user clustering is proposed. Given a current controversial political topic, the Louvain Modularity algorithm is used to detect communities of users sharing the same political preferences. The political alignment of a set of users is labeled manually by a human expert and then the quality of the community detection is evaluated against this gold standard. In the last section, we propose a novel force-directed graph algorithm to generate a visual representation of the detected communities.

Non-parametric Bayesian graph models reveal community structure in resting state fMRI

Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite Diagonal Model (IDM). The models define probabilities of generating links within and between clusters and the difference between the models lies in the restrictions they impose upon the between-cluster link probabilities. IRM is the most flexible model with no restrictions on the probabilities of links between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background noise probability. These probabilistic models are compared against three other approaches for node clustering, namely Infomap, Louvain modularity, and hierarchical clustering. Using 3 different datasets comprising healthy volunteers' rs-fMRI we found that the BCD model was in general the most predictive and reproducible model. This suggests that rs-fMRI data exhibits community structure and furthermore points to the significance of modeling heterogeneous between-cluster link probabilities.