Urban community structure detection based on the OD of traffic analysis zones

2019 ◽  
Vol 33 (13) ◽  
pp. 1950164
Author(s):  
Qing-Feng Dong ◽  
Dian-Kun Chen ◽  
Ting Wang
Keyword(s):  
Community Structure ◽  
Quantitative Methods ◽  
Urban Community ◽  
Spatial Location ◽  
Traffic Analysis ◽  
Association Mining ◽  
Community Structures ◽  
City Community ◽  
Traffic Characteristics ◽  
Structure Detection

At present, the detection of urban community structures is mainly based on existing administrative divisions, and is performed using qualitative methods. The lack of quantitative methods makes it difficult to judge the rationality of urban community divisions. In this study, we used complex network association mining methods to detect a city community structure by using the Origin-Destinations (OD) at traffic analysis zone (TAZ) level, and successively assigned all the TAZs into different communities. Based on the community results, we calculated the community core degree of each TAZ within every community, and then calculated the Traffic Core Degree and Location Core Degree indicators of the community based on OD passenger flow and spatial location relationship between communities. Finally, we analyzed the correlation among three indicators to ensure the rationality of the community structure. We used the city of Zhengzhou in 2016 as an example case study. For Zhengzhou, we detected a total of six communities. We found a relatively low correlation between Traffic Core Degree and Location Core Degree. Within each group, the correlation between community core degree and Traffic Core Degree was higher than that between community core degree and Location Core Degree, indicating that the urban community structure is more reasonably based on traffic characteristics. The development of a quantitative approach for determining reasonable city community structures has important implications for transportation planning and industrial layout.

Community Detection in Political Discussions on Twitter

2016 ◽  
Vol 35 (2) ◽  
pp. 244-261 ◽  
Author(s):  
Frederic Guerrero-Solé
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Reliable Method ◽  
Community Structures ◽  
Network Community ◽  
High Polarization

In November 9, 2014, the Catalan government called Catalan people to participate in a straw poll about the independence of Catalonia from Spain. This article analyzes the use of Twitter between November 8 and 10, 2014. Drawing on a methodology developed by Guerrero-Solé, Corominas-Murtra, and Lopez-Gonzalez, this work examines the structure of the retweet overlap network (RON), formed by those users whose communities of retweeters have nonzero overlapping, to detect the community structure of the network. The results show a high polarization of the resulting network and prove that the RON is a reliable method to determinate network community structures and users’ political leaning in political discussions.

Community Structure Detection Using Firefly Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 52-70 ◽  
Author(s):  
Ameera Saleh Jaradat ◽  
Safa'a Bani Hamad
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Graph Partitioning ◽  
Firefly Algorithm ◽  
Real Life ◽  
People Detection ◽  
Continuous Growth ◽  
Structure Detection ◽  
Partitioning Problem ◽  
And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

scholarly journals Taxonomic Compositions and Co-occurrence Relationships of Protists in Bulk Soil and Rhizosphere of Soybean Fields in Different Regions of China

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Zhang ◽  
Pengcheng Xing ◽  
Mengyu Niu ◽  
Gehong Wei ◽  
Peng Shi
Keyword(s):  
High Throughput Sequencing ◽  
Niche Overlap ◽  
Spatial Location ◽  
Bulk Soil ◽  
Community Structures ◽  
Potential Predator ◽  
Predator Prey ◽  
Soybean Plants ◽  
Bacteria And Fungi ◽  
Potential Interactions

As the main consumers of bacteria and fungi in farmed soils, protists remain poorly understood. The aim of this study was to explore protist community assembly and ecological roles in soybean fields. Here, we investigated differences in protist communities using high-throughput sequencing and their inferred potential interactions with bacteria and fungi between the bulk soil and rhizosphere compartments of three soybean cultivars collected from six ecological regions in China. Distinct protist community structures characterized the bulk soil and rhizosphere of soybean plants. A significantly higher relative abundance of phagotrophs was observed in the rhizosphere (25.1%) than in the bulk soil (11.3%). Spatial location (R2 = 0.37–0.51) explained more of the variation in protist community structures of soybean fields than either the compartment (R2 = 0.08–0.09) or cultivar type (R2 = 0.02–0.03). The rhizosphere protist network (76 nodes and 414 edges) was smaller and less complex than the bulk soil network (147 nodes and 880 edges), indicating a smaller potential of niche overlap and interactions in the rhizosphere due to the increased resources in the rhizosphere. Furthermore, more inferred potential predator-prey interactions occur in the rhizosphere. We conclude that protists have a crucial ecological role to play as an integral part of microbial co-occurrence networks in soybean fields.

scholarly journals High-throughput amplicon sequencing reveals the spatiotemporal effects, abiotic and biotic shaping factors for the microbial communities in tropical mangrove sediments in Sanya, China

2020 ◽  
Author(s):  
Wu Qu ◽  
Boliang Gao ◽  
Jie Wu ◽  
Min Jin ◽  
Jianxin Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Microbial Communities ◽  
Fungal Community ◽  
Amplicon Sequencing ◽  
Biotic Factors ◽  
Community Structures ◽  
Mangrove Sediments ◽  
Element Cycling ◽  
Microbial Community Structures

Abstract Background Microbial roles in element cycling and nutrient providing are crucial for mangrove ecosystems and serve as important regulators for climate change in Earth ecosystem. However, some key information about the spatiotemporal influences and abiotic and biotic shaping factors for the microbial communities in mangrove sediments remains lacking. Methods In this work, 22 sediment samples were collected from multiple spatiotemporal dimensions, including three locations, two depths, and four seasons, and the bacterial, archaeal, and fungal community structures in these samples were studied using amplicon sequencing. Results The microbial community structures were varied in the samples from different depths and locations based on the results of LDA effect size analysis, principal coordinate analysis, the analysis of similarities, and permutational multivariate ANOVA. However, these microbial community structures were stable among the seasonal samples. Linear fitting models and Mantel test showed that among the 13 environmental factors measured in this study, the sediment particle size (PS) was the key abiotic shaping factor for the bacterial, archaeal, or fungal community structure. Besides PS, salinity and humidity were also significant impact factors according to the canonical correlation analysis (p ≤ 0.05). Co-occurrence networks demonstrated that the bacteria assigned into phyla Ignavibacteriae, Proteobacteria, Bacteroidetes, Chloroflexi, and Acidobacteria were the key biotic factors for shaping the bacterial community in mangrove sediments. Conclusions This work showed the variability on spatial dimensions and the stability on temporal dimension for the bacterial, archaeal, or fungal microbial community structure, indicating that the tropical mangrove sediments are versatile but stable environments. PS served as the key abiotic factor could indirectly participate in material circulation in mangroves by influencing microbial community structures, along with salinity and humidity. The bacteria as key biotic factors were found with the abilities of photosynthesis, polysaccharide degradation, or nitrogen fixation, which were potential indicators for monitoring mangrove health, as well as crucial participants in the storage of mangrove blue carbons and mitigation of climate warming. This study expanded the knowledge of mangroves for the spatiotemporal variation, distribution, and regulation of the microbial community structures, thus further elucidating the microbial roles in mangrove management and climate regulation.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Variations in microbial community structure and functional gene expression in bio-treatment processes with odorous pollutants

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Weidong Li ◽  
Jianguo Ni ◽  
Shaoqin Cai ◽  
Ying Liu ◽  
Chenjia Shen ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Microbial Community Structure ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Community Structures ◽  
Industrial Emissions ◽  
Gas Treatment ◽  
Kegg Pathways ◽  
Microbial Community Structures

AbstractEngineered microbial ecosystems in biofilters have been widely applied to treat odorous gases from industrial emissions. Variations in microbial community structure and function associated with the removal of odorous gases by biofilters are largely unknown. This study performed a metagenomic analysis to discover shifts in microbial community structures in a commercial scale biofilter after treating odorous gas. Our study identified 175,675 functional genes assigned into 43 functional KEGG pathways. Based on the unigene sequences, there were significant changes in microbial community structures in the biofilter after treating odorous gas. The dominant genera were Thiobacillus and Oceanicaulis before the treatment, and were Acidithiobacillus and Ferroplasma after the treatment. A clustering analysis showed that the number of down-regulated microbes exceeded the number of up-regulated microbes, suggesting that odorous gas treatment reduced in microbial community structures. A differential expression analysis identified 29,975 up- and 452,599 down-regulated genes. An enrichment analysis showed 17 classic types of xenobiotic biodegradation pathways. The results identified 16 and 15 genes involved in ammonia and sulfite metabolism, respectively; an analysis of their relative abundance identified several up-regulated genes, which may be efficient genes involved in removing odorous gases. The data provided in this study demonstrate the changes in microbial communities and help identify the dominant microflora and genes that play key roles in treating odorous gases.

Host identity is more important in structuring bacterial epiphytes than endophytes in a tropical mangrove forest

2020 ◽  
Vol 96 (4) ◽  
Author(s):  
Hui Yao ◽  
Xiang Sun ◽  
Chao He ◽  
Xing-Chun Li ◽  
Liang-Dong Guo
Keyword(s):  
Community Structure ◽  
Endophytic Bacteria ◽  
Illumina Miseq ◽  
Rrna Gene ◽  
Network Structures ◽  
Community Structures ◽  
Mangrove Species ◽  
Host Identity ◽  
Biodiversity Maintenance ◽  
Endophytic Bacterial Community

ABSTRACT Interactions between plants and microbes are involved in biodiversity maintenance, community stability and ecosystem functioning. However, differences in the community and network structures between phyllosphere epiphytic and endophytic bacteria have rarely been investigated. Here, we examined phyllosphere epiphytic and endophytic bacterial communities of six mangrove species using Illumina MiSeq sequencing of the 16S rRNA gene. The results revealed that the community structure of epiphytic and endophytic bacteria was different. Plant identity significantly affected the diversity and community structure of both epiphytic and endophytic bacteria, with a greater effect on the community structure of the former than the latter. Network analysis showed that both plant–epiphytic and plant–endophytic bacterial network structures were characterized by significantly highly specialized and modular but lowly connected and anti-nested properties. Furthermore, the epiphytic bacterial network was more highly specialized and modular but less connected and more strongly anti-nested than the endophytic bacterial network. This study reveals that the phyllosphere epiphytic and endophytic bacterial community structures differ and plant identity has a greater effect on the epiphytic than on the endophytic bacteria, which may provide a comprehensive insight into the role of plant identity in driving the phyllosphere epiphytic and endophytic microbial community structures in mangrove ecosystems.

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