Louvain Community Detection Algorithm Research Articles

Subtyping drug-free first-episode major depressive disorder based on cortical surface area alterations

BackgroundMajor depressive disorder (MDD) is recognized as a complex and heterogeneous metal illness, characterized by diverse clinical symptoms and variable treatment outcomes. Previous studies have repeatedly reported alterations in brain morphology in MDD, but findings vary across sample characteristics. Whether this neurobiological substrate could stratify MDD into more homogeneous clinical subgroups thus improving personalized medicine remains unknown. MethodsWe included 65 drug-free patients with first-episode MDD and 66 healthy controls (HCs) and collected their structural MRI data. We performed the surface reconstruction and calculated cortical surface area using Freesurfer. The surface area of 34 Gy matter regions in each hemisphere based on the Desikan-Killiany atlas were extracted for each participant and subtyping results were obtained with the Louvain community detection algorithm. The demographic and clinical characteristics were then compared between MDD subgroups. ResultsTwo subgroups defined by distinct patterns of cortical surface area were identified in first-episode MDD. Subgroup 1 exhibited a significant reduction in surface area across nearly the entire cortex compared to subgroup 2 and HCs, whereas subgroup 2 demonstrated increased surface area than HCs. Further, subgroup 1 exhibited a higher proportion of females, and higher severity of anxiety symptoms compared to subgroup 2. LimitationsThe relatively small sample size. ConclusionsThis study identified two neurobiologically subgroups with distinct alterations in cortical surface area among drug-free patients with first-episode MDD. Our results highlight the promise of in delineating morphological heterogeneity within MDD, particularly in relation to the severity of anxiety symptoms.

Defence Partnerships and Economic Dynamics: An Analysis in PESCO Countries

ABSTRACT This paper employs a panel vector autoregressive (PVAR) approach to investigate the relationship among military expenditure, investment, and economic growth, over the period after the enforcement of the Maastricht treaty (1994–2022) in 25 European countries that participate in the Permanent Structured Cooperation (PESCO). By using the Louvain community detection algorithm on the network links that have been established through defence partnerships in PESCO projects, two different country clusters emerge. Findings suggest that military expenditures can stimulate economic growth but the effects may not be common for all Member States, which might benefit from the involvement in joint defence projects to maximize the effectiveness of their defence spending.

Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.

Media frames and images of Chinese immigrants: analyzing European newspaper narratives from 2015 to 2022

The influx of Chinese immigrants into Europe has spawned varied media frames, shaping public perceptions and attitudes towards this demographic. The portrayal of Chinese immigrants in European newspapers has become a subject of socio-political discourse, necessitating exploring the predominant narratives within the media space. This study aims to unearth and analyze the prevailing frames used in European newspapers when reporting on Chinese immigrants from 2015 to 2022, elucidating the impact of these frames on the constructed images of Chinese immigrants in societal consciousness. Employing an inductive mixed-method computational approach (ANTMN) and leveraging the Lexis database, 360 news reports from European newspapers were analyzed using Latent Dirichlet Allocation (LDA) and Gibbs Sampling for topic modeling, semantic network procedures for network structure, and Louvain community detection algorithm for clustering consistent framework packages. The study identifies four primary frames—Integration, Social, Political, and Crime—each revealing a distinct narrative about Chinese immigrants. Furthermore, three predominant images of Chinese immigrants emerged: Failed Integrators, Targets of Political Attack, and Disruptors of Social Safety. Despite the decrease in the utilization of the Crime frame amidst the COVID-19 pandemic, an intensified prejudice against Chinese immigrants was noted. The identified frames and images have crucial implications on socio-political attitudes and policies concerning immigrants, emphasizing the necessity to address the media’s role in potentially perpetuating stereotypes and biases, thereby influencing societal coherence and policy approaches toward immigration.

A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature

This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks. By integrating the Louvain community detection algorithm into our GNN frameworks, we consistently enhanced the performance across all models tested. For example, integrating the Louvain model with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains were noted when the Louvain model was paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent increase in performance—reflected in our extensive experimentation on bipartite graphs of scientific collaborations and citations—highlights the synergistic potential of combining community detection with GNNs to overcome common link prediction challenges such as scalability and resolution limits. Our findings advocate for the integration of community structures as a significant step forward in the predictive accuracy of network science models, offering a comprehensive understanding of scientific collaboration patterns through the lens of advanced machine learning techniques.

A Data-Driven Rutting Depth Short-Time Prediction Model with Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters.

Network Analysis of Academic Medical Center Websites in the United States

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

Community detection in brain connectomes with hybrid quantum computing

Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap’s Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.

Improving commuting zones using the Louvain community detection algorithm

Well-defined commuting zones are essential for accurate research on US local labor markets. To develop commuting zones, one must construct edge weights – a measure of commuting flows between counties – and then use the edge weights to partition counties into clusters. I improve upon currently used “ERS” commuting zones in two ways. First, it is unclear if ERS commuting zones use the best edge weights. Therefore, I test multiple edge weights. Second, the algorithm to produce ERS commuting zones requires specifying a theoretically-unguided cutoff parameter; results may be sensitive to the parameter choice. Instead, I use the Louvain algorithm, which optimizes for “modularity”, a graph-intrinsic parameter that is greater when there is higher intra-commuting zone flow and lower inter-commuting zone flow. I call my new delineations “TS Louvain”, which uses the ERS commuting flow definition to construct edge weights, and “Sum Louvain”, which uses the total number of commuters as edge weights. Compared to ERS, TS Louvain and Sum Louvain have 0.05 to 0.15 greater modularity, Sum Louvain has a 0.01 to 0.02 higher share of people who work and live in the same commuting zone, and in a case study, TS Louvain produces greater estimates and t-statistics. These metrics suggest that these new commuting zones improve upon the existing delineations. Researchers can access these commuting zone definitions at bit.ly/LouvainCZ . • Two new delineations of commuting zones are produced using the Louvain algorithm. • New delineations Sum Louvain and TS Louvain improve upon existing delineations. • Sum Louvain has higher share of those who work and live in the same commuting zone. • In a case study, TS Louvain produces larger estimates and t-statistics.

Scalable multi‐node multi‐GPU Louvain community detection algorithm for heterogeneous architectures

SummaryCommunity detection is an important problem that is widely applied for finding cluster patterns in brain, social, biological, and many other kinds of networks. In this work, we have developed a multi‐node multi‐GPU Louvain community detection algorithm, simultaneously harnessing the CPU and GPU cores of the devices. The algorithm partitions a given graph across multiple nodes and devices in the nodes and performs independent computations of Louvain algorithm on the parts on the devices. The independently formed communities in the devices are refined by identification of doubtful vertices and migrating them to the other processors. The communities are merged using a hierarchical merging algorithm that ensures that at any point the merged component can be accommodated within a processor. Our experiments show that our algorithm is highly scalable with increasing number of devices and provides large‐scale performance for BigData graphs.

An automatic integrative method for learning interpretable communities of biological pathways.

Although knowledge of biological pathways is essential for interpreting results from computational biology studies, the growing number of pathway databases complicates efforts to efficiently perform pathway analysis due to high redundancies among pathways from different databases, and inconsistencies in how pathways are created and named. We introduce the PAthway Communities (PAC) framework, which reconciles pathways from different databases and reduces pathway redundancy by revealing informative groups with distinct biological functions. Uniquely applying the Louvain community detection algorithm to a network of 4847 pathways from KEGG, REACTOME and Gene Ontology databases, we identify 35 distinct and automatically annotated communities of pathways and show that they are consistent with expert-curated pathway categories. Further, we demonstrate that our pathway community network can be queried with new gene sets to provide biological context in terms of related pathways and communities. Our approach, combined with an interpretable web tool we provide, will help computational biologists more efficiently contextualize and interpret their biological findings.

Cognitive Analysis of Auditory Temporal Sequence Information Difference Based on Network Switching

Auditory sense is an important way for people to receive and interact with foreign information. In different environment, the auditory sense changes. Therefore, it is necessary to find a detection method that can detect hearing in a timely manner. In this paper, EEG experiments were used to construct and compare brain functional networks in different states, and auditory state models were constructed with different auditory input signals. Secondly, the cross-correlation method is used to slice the signal and construct the adjacency matrix. Louvain community detection algorithm is used to process the data and calculate the network conversion rate under different parameters. It is concluded that the network conversion rate can be used to analyze the temporal variation of auditory information under the condition of controlled parameters. This indicates that the network conversion rate can also be used as a method to analyze auditory signals in the future.

Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection

To study the deformation characteristics of reverse slope, this paper took the slope of Xiaodongcao as the research object, applied the Louvain community detection algorithm, considered the influence of reservoir water level change, and partitioned the slope deformation characteristics. The deformation characteristic zoning result was superimposed with the slope displacement cloud map and three types of geological geometric characteristic factor zoning map obtained by ArcGIS. The results show that: community detection can quickly identify the closely connected part of slope network, and the specific location of this part is affected by reservoir water. After the community detection result is superimposed with the displacement cloud map, the areas with large deformation and close connection in the slope can be identified. It is found that the community with severe deformation has at least 5% more displacement and up to 21% more displacement than that with slow deformation. In addition, the location of leader nodes can be identified, and the number of leader nodes does not exceed 20% of the total nodes in the community, and its average displacement is at least 10% more than that of ordinary nodes, up to 36%. After the community detection result is superimposed with the zoning map of slope grade, it can be concluded that the slope grade within the community with severe deformation is greater than 60°, indicating that the larger slope grade is more sensitive to the bank slope deformation.

Scalable distributed Louvain algorithm for community detection in large graphs

Scalable distributed Louvain algorithm for community detection in large graphs

Parallel Louvain Community Detection Algorithm Based on Dynamic Thread Assignment on Graphic Processing Unit

Background and Objectives: Louvain is a time-consuming community detection algorithm especially in large-scale networks. Using Graphic Processing Unit (GPU) in order to calculate modularity sigma, which is a major processing section in Louvain algorithm, can reduce algorithm execution time and make it practical for large-scale networks.Methods: The proposed algorithm Dynamic CUDA Louvain Method (DCLM) blocks hardware threads dynamically on cores inside GPU. By considering the properties of GPU, this algorithm allocates the maximal number of processing cores to each Stream Multi-Processor (SM) as number of threads in a block. If the number of nodes in the graph is smaller than all physical cores on GPU, number of threads per block Is equal to the ratio number of graph nodes over the number of SMs.Results: The implementation results demonstrated that the proposed algorithm is able to decrease the run time by 15% in comparison with the best past method in the large-scale graph.Conclusion: We have introduced DCLM algorithm based on GPU that accelerates Louvain community detection algorithm. Dynamic allocation of threads to each block has a significant effect on the reduction of algorithm execution time. However, incrementing the number of threads per block alone does not result to acceleration the speed of calculations.

An Improved Louvain Algorithm for Community Detection

Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.

Risk Management in Seaports: A Community Analysis at the Port of Hamburg

The aim of this work is to detect communities of stakeholders at the port of Hamburg regarding their communication intensity in activities related to risk management. An exploratory mixed-method design is chosen as a methodology based on a compact survey and semi-structured interviews, as well as secondary data. A compact survey at the port of Hamburg is utilized to address the communication intensity values among stakeholders. Based on 28 full responses, the data is extracted, cleansed, and prepared for the network analysis using the software “Gephi”. Thereafter, the Louvain community detection algorithm is used to extract the communities from the network. A plausibility check is carried out using 15 semi-structured interviews and secondary data to verify and refine the results of the community analysis. The results have revealed different communities for the following risk categories: (a) natural disasters and (b) operational and safety risks. The focus of cooperation is on the reactive process and emergency plans. For instance, emergency plans play an important role in the handling of natural disasters such as floods or extreme winds.

Neighborhood-based bridge node centrality tuple for complex network analysis

We define a bridge node to be a node whose neighbor nodes are sparsely connected to each other and are likely to be part of different components if the node is removed from the network. We propose a computationally light neighborhood-based bridge node centrality (NBNC) tuple that could be used to identify the bridge nodes of a network as well as rank the nodes in a network on the basis of their topological position to function as bridge nodes. The NBNC tuple for a node is asynchronously computed on the basis of the neighborhood graph of the node that comprises of the neighbors of the node as vertices and the links connecting the neighbors as edges. The NBNC tuple for a node has three entries: the number of components in the neighborhood graph of the node, the algebraic connectivity ratio of the neighborhood graph of the node and the number of neighbors of the node. We analyze a suite of 60 complex real-world networks and evaluate the computational lightness, effectiveness, efficiency/accuracy and uniqueness of the NBNC tuple vis-a-vis the existing bridgeness related centrality metrics and the Louvain community detection algorithm.

Community detection in spatial correlation graphs: Application to non-stationary ground motion modeling

In this paper, we propose a community detection method to find regions in spatial data with higher correlations. We construct a ‘correlation deviation graph’ that considers the influence of global correlation caused by the node's geographical location. We compare the performance of the correlation deviation graph to conventional correlation graph construction approaches for community detection, using synthetic data, and show that the proposed method has the best performance in the presence of a global correlation structure. We also compare the performance of signed spectral clustering and the signed Louvain algorithm for community detection on the correlation graphs, and find that the signed spectral clustering algorithm performs best. We apply our algorithms to simulated earthquake ground motion data in the Los Angeles region. The results suggest that communities of high correlation in ground shaking tend to be associated with common geological conditions and relative location along the rupture strike direction.

Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model.

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter . Therefore, we can simply set or for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.