ON THE ASYMPTOTICS OF CLUSTERING COEFFICIENT IN A CONFIGURATION GRAPH WITH UNKNOWN DISTRIBUTION OF VERTEX DEGREES

2019 ◽  
Keyword(s):  
Clustering Coefficient ◽  
Unknown Distribution ◽  
Vertex Degrees

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

scholarly journals Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 970
Author(s):  
Maedeh Khalilian ◽  
Kamran Kazemi ◽  
Mahshid Fouladivanda ◽  
Malek Makki ◽  
Mohammad Sadegh Helfroush ◽  
...  
Keyword(s):  
Human Brain ◽  
Clustering Coefficient ◽  
Quality Data ◽  
B Values ◽  
Rich Club ◽  
Global Efficiency ◽  
Acquisition Strategy ◽  
The Rich ◽  
Structural Networks ◽  
Single Shell

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

scholarly journals Effects of COVID-19 on Urban Population Flow in China

2021 ◽  
Vol 18 (4) ◽  
pp. 1617 ◽  
Author(s):  
Xiaorong Jiang ◽  
Wei Wei ◽  
Shenglan Wang ◽  
Tao Zhang ◽  
Chengpeng Lu
Keyword(s):  
Network Topology ◽  
Traffic Control ◽  
Urban Population ◽  
Deep Understanding ◽  
Clustering Coefficient ◽  
Spatial Distance ◽  
Structure Evolution ◽  
Effective Distance ◽  
Urban Hierarchy ◽  
The City

The COVID-19 epidemic has become a Public Health Emergency of International Concern. Thus, this sudden health incident has brought great risk and pressure to the city with dense population flow. A deep understanding of the migration characteristics and laws of the urban population in China will play a very positive role in the prevention and control of the epidemic situation. Based on Baidu location-based service (LBS) big data, using complex networks method and geographic visualization tools, this paper explores the spatial structure evolution of population flow network (PFN) in 368 cities of China under different traffic control situations. Effective distance models and linear regression models were established to analyze how the population flow across cities affects the spread of the epidemic. Our findings show that: (1) the scope of population flow is closely related to the administrative level of the city and the traffic control policies in various cities which adjust with the epidemic situation; The PFN mainly presents the hierarchical structure dominated by the urban hierarchy and the regional isolation structure adjacent to the geographical location.(2) through the analysis network topology structure of PFN, it is found that only the first stage has a large clustering coefficient and a relatively short average path length, which conforms to the characteristics of small world network. The epidemic situation has a great impact on the network topology in other stages, and the network structure tends to be centralized. (3) The overall migration scale of the whole country decreased by 36.85% compared with the same period of last year’s lunar calendar, and a further reduction of 78.52% in the nationwide traffic control stage after the festival. (4) Finally, based on the comparison of the effective distance and the spatial distance from the Wuhan to other destination cities, it is demonstrated that there is a higher correlation between the effective distance and the epidemic spread both in Hubei province and the whole country.

scholarly journals Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 207
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Elena Sánchez-López ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Complex Networks ◽  
Global Climate ◽  
Spatial Relation ◽  
Clustering Coefficient ◽  
Horizontal Visibility ◽  
Climate Conditions ◽  
Mean Values ◽  
Intrinsic Nature ◽  
Temperature Anomalies ◽  
Mean Temperature

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

scholarly journals Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome

2019 ◽  
Vol 8 (3) ◽  
pp. 306 ◽  
Author(s):  
Alberto Cacciola ◽  
Antonino Naro ◽  
Demetrio Milardi ◽  
Alessia Bramanti ◽  
Leonardo Malatacca ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Topology ◽  
Minimally Conscious State ◽  
Conscious State ◽  
Clustering Coefficient ◽  
Unresponsive Wakefulness Syndrome ◽  
Topological Network ◽  
Functional Brain Network ◽  
Minimally Conscious ◽  
Nodal Level

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.

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