AUTONOMY-ORIENTED SOCIAL NETWORKS MODELING: DISCOVERING THE DYNAMICS OF EMERGENT STRUCTURE AND PERFORMANCE

2007 ◽  
Vol 21 (04) ◽  
pp. 611-638 ◽  
Author(s):  
SHIWU ZHANG ◽  
JIMING LIU
Keyword(s):  
Social Network ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Clustering Coefficient ◽  
Local Interaction ◽  
Selection Strategies ◽  
Real World Applications ◽  
Emergent Structure ◽  
And Performance ◽  
Dynamic Relationships

A social network is composed of social individuals and their relationships. In many real-world applications, such a network will evolve dynamically over time and events. A social network can be naturally viewed as a multiagent system if considering locally-interacting social individuals as autonomous agents. In this paper, we present an Autonomy-Oriented Computing (AOC) based model of a social network, and study the dynamics of the network based on this model. In the AOC model, the profile of agents, service-based interactions, and the evolution of the network are defined, and the autonomy of the agents is emphasized. The model can reveal dynamic relationships among global performance, local interaction (partner selection) strategies, and network topology. The experimental results show that the agent network forms a community with a high clustering coefficient, and the performance of the network is dynamically changing along with the formation of the network and the local interaction strategies of the agents. In this paper, the performance and topology of the agent network are analyzed, and the factors that affect the performance and evolution of the agent network are examined.

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.

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 Identifying Integration and Differentiation in a Hospital’s Logistical System: A social network analysis of a case study

2020 ◽  
Author(s):  
Annelies van der Ham ◽  
Frits Van Merode ◽  
Dirk Ruwaard ◽  
Arno Van Raak
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Organization Theory ◽  
Exploratory Study ◽  
Case Study Research ◽  
Study Research ◽  
And Performance

Abstract Background Integration, the coordination and alignment of tasks, has been promoted widely in order to improve the performance of hospitals. Both organization theory and social network analysis offer perspectives on integration. This exploratory study research aims to understand how a hospital’s logistical system works, and in particular to what extent there is integration and differentiation. More specifically, it first describes how a hospital organizes logistical processes; second, it identifies the agents and the interactions for organizing logistical processes, and, third, it establishes the extent to which tasks are segmented into subsystems, which is referred to as differentiation, and whether these tasks are coordinated and aligned, thus achieving integration.Methods The study is based on case study research carried out in a hospital in the Netherlands. All logistical tasks that are executed for surgery patients were studied. Using a mixed method, data were collected from the Hospital Information System (HIS), documentation, observations and interviews. These data were used to perform a social network analysis and calculate the network metrics of the hospital network.Results This paper shows that 23 tasks are executed by 635 different agents who interact through 31,499 interaction links. The social network of the hospital demonstrates both integration and differentiation. The network appears to function differently from what is assumed in literature, as the network does not reflect the formal organizational structure of the hospital, and tasks are mainly executed across functional silos. Nurses and physicians perform integrative tasks and two agents who mainly coordinate the tasks in the network, have no hierarchical position towards other agents. The HIS does not seem to fulfill the interactional needs of agents. Conclusions This exploratory study reveals the network structure of a hospital. The cross-functional collaboration, the integration found, and position of managers, coordinators, nurses and doctors suggests a possible gap between organizational perspectives on hospitals and reality. This research sets a basis for further research that should focus on the relation between network structure and performance, on how integration is achieved and in what way organization theory concepts and social network analysis could be used in conjunction with one another.

Efficient inference for agent-based models of real-world phenomena

2021 ◽  
Author(s):  
Andreas Christ Sølvsten Jørgensen ◽  
Atiyo Ghosh ◽  
Marc Sturrock ◽  
Vahid Shahrezaei
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Parameter Space ◽  
Real World ◽  
Autonomous Agents ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Learning Approaches ◽  
Real World Applications ◽  
Real World Problems

AbstractThe modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners.Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

scholarly journals Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaolong Deng ◽  
Hao Ding ◽  
Yong Chen ◽  
Cai Chen ◽  
Tiejun Lv
Keyword(s):  
Social Network ◽  
Vulnerability Assessment ◽  
Online Social Networks ◽  
Information Transfer ◽  
Large Scale ◽  
Real Life ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Directed Network ◽  
Online Social Media

In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and I M p v (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for I M p v . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and I M p v based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of I M p v . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

The Evolution of Coworker Networks

2008 ◽  
pp. 280-293 ◽  
Author(s):  
Arianna Dal Forno
Keyword(s):  
Social Network ◽  
Work Team ◽  
Combinatorial Explosion ◽  
Team Members ◽  
Team Selection ◽  
The Social ◽  
Two Factors ◽  
Human Experiments ◽  
And Performance ◽  
Selection Agent

When selecting work team members several behavioral components concur. In this chapter we are interested in investigating the effects of these components in terms of team selection, agent aggregation and performance of groups. A computational model, together with a theoretical approach and the results of two human experiments where subjects interact in a similar game, allow us to identify some of the most important determinants. Our results suggest that the occurrence of two factors is crucial: the presence of leaders as aggregators of knowledge and agents being able to expand and improve their higher profit projects. It is particularly evident the threefold role the leaders have. First, they increase the social network of other agents making possible projects otherwise impossible. Second, they state the pace of a balanced growth in terms of social network, while taming the otherwise combinatorial explosion. Finally, they help selecting one of the theoretically possible equilibria.

scholarly journals Octodon degus kin and social structure

2015 ◽  
Vol 97 (2) ◽  
pp. 361-372 ◽  
Author(s):  
Garrett T. Davis ◽  
Rodrigo A. Vásquez ◽  
Elie Poulin ◽  
Esteban Oda ◽  
Enrique A. Bazán-León ◽  
...  
Keyword(s):  
Social Network ◽  
Genetic Relatedness ◽  
Social Groups ◽  
Clustering Coefficient ◽  
Habitat Characteristics ◽  
Redes Sociales ◽  
Octodon Degus ◽  
Local Conditions ◽  
Network Parameters ◽  
Los Grupos

Abstract A growing body of evidence showing that individuals of some social species live in non-kin groups suggests kin selection is not required in all species for sociality to evolve. Here, we investigate 2 populations of Octodon degus , a widespread South American rodent that has been shown to form kin and non-kin groups. We quantified genetic relatedness among individuals in 23 social groups across 2 populations as well as social network parameters (association, strength, and clustering coefficient) in order to determine if these aspects of sociality were driven by kinship. Additionally, we analyzed social network parameters relative to ecological conditions at burrow systems used by groups, to determine if ecological characteristics within each population could explain variation in sociality. We found that genetic relatedness among individuals within social groups was not significantly higher than genetic relatedness among randomly selected individuals in both populations, suggesting that non-kin structure of groups is common in degus. In both populations, we found significant relationships between the habitat characteristics of burrow systems and the social network characteristics of individuals inhabiting those burrow systems. Our results suggest that degu sociality is non-kin based and that degu social networks are influenced by local conditions. Es creciente la evidencia que apoya la ocurrencia de especies sociales donde los individuos no están emparentados genéticamente, lo que sugiere que la selección de parentesco no es indispensable para la evolución de la sociabilidad. En este estudio se examinaron dos poblaciones de Octodon degus , un roedor sudamericano donde los grupos sociales pueden o no incluir individuos cercanamente emparentados. Se cuantificó el parentesco genético entre individuos en 23 grupos sociales y en redes sociales de dos poblaciones para determinar si estos aspectos de la sociabilidad dependen del grado de parentesco. Además, se examinaron asociaciones entre los parámetros cuantificados de las redes sociales (asociación, fuerza, coeficiente de anidamiento) y las condiciones ecológicas a nivel de los sistemas de madriguera usados por cada grupo. El grado de parentesco genético dentro de los grupos no fue distinto del grado de parentesco entre individuos de la población tomados al azar, lo que apoya que una estructura de grupos no emparentada es la regla en Octodon degus . En ambas poblaciones se registró una asociación entre características ecológicas de los sistemas de madriguera y atributos de las redes sociales de los individuos que usan estas estructuras. Nuestros resultados indican que la sociabilidad en Octodon degus no está basada en relaciones de parentesco y que las redes sociales de estos animales dependen de las condiciones ecológicas.

Simulation of Longitudinal Train Dynamics: Case Studies Using the Train Energy and Dynamics Simulator (TEDS)

2015 ◽  
Author(s):  
Monique F. Stewart ◽  
S. K. (John) Punwani ◽  
David R. Andersen ◽  
Graydon F. Booth ◽  
Som P. Singh ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Case Studies ◽  
State Of The Art ◽  
Longitudinal Dynamics ◽  
Simulation Tools ◽  
Power Operations ◽  
Real World Applications ◽  
Train Dynamics ◽  
And Performance ◽  
Train Safety

Longitudinal dynamics influence several measures of train performance, including schedules and energy efficiency, stopping distances, run-in/run-out forces, etc. Therefore, an effective set of tools for studying longitudinal dynamics is essential to improving the safety and performance of train operations. Train Energy and Dynamics Simulator (TEDS) is a state-of-the-art software program designed and developed by the Federal Railroad Administration (FRA), for studying and simulating train safety and performance, and can be used for modeling train performance under a wide variety of equipment, track, and operating configurations [1]. Several case studies and real-world applications of TEDS, including the investigation of multiple train make-up and train handling related derailments, a study of train stopping distances, evaluations of the safety benefits of Electronically Controlled Pneumatic (ECP) brakes, Distributed Power operations, and a study of alternate train handling methodologies are described in this paper. These studies demonstrate the effectiveness of using the appropriate simulation tools to quantify and enhance a better understanding of train dynamics, and the resultant safety benefits.

Detection of Leading Experts from ResearchGate

2018 ◽  
Vol 5 (3) ◽  
pp. 67-86
Author(s):  
Eya Ben Ahmed
Keyword(s):  
Social Media ◽  
Social Network ◽  
Technological Development ◽  
Clustering Method ◽  
Web Based ◽  
Supervised Clustering ◽  
And Performance

This article describes how thanks to the technological development, social media has propagated in recent years. The latter describes a range of Web-based platforms that enable people to socially interact with one another online. Several types of social media appeared. In this context, the author focuses on scientific social network which connects the researchers and allow them to communicate and collaborate online. In this paper, we, particularly, aim to detect the scientific leaders through firstly detect communities in social network then identify the leader of each group. To do this, the author introduces a new hierarchical semi-supervised clustering method based on ordinal density. The results of carried out experiments on real scientific warehouse have shown significant profits in terms of accuracy and performance.

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