scholarly journals Network Analysis as a Powerful Approach to Analyse the Operational Environment

2020 ◽  
Vol 25 (3) ◽  
pp. 245-252
Author(s):  
Martin Hristov
Keyword(s):  
Graph Theory ◽  
Network Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Operational Environment ◽  
Powerful Approach ◽  
Alternative Approaches

AbstractIn times of information flooding us from everywhere and in an environment of increasingly complex and difficult to understand systems that our forces face in the operational environment, there is a need for alternative approaches to analyzing the environment and its participants. This approach meets key requirements and provides new and in-depth knowledge of systems. Network analysis is an approach based on graph theory that is applicable to the analysis of any system or process with a network structure. The network analysis toolkit is suitable for highlighting “important” players or elements in complex networks invisible at first glance, according to various criteria set in the term “important”.

scholarly journals Representation of Graph Theory in Students’ Communication Network at Female Students’ Dormitory of State Islamic Institute of Palopo

CAUCHY ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 95
Author(s):  
Muhammad Hajarul Aswad ◽  
Wahyuni Husain
Keyword(s):  
Communication Network ◽  
Graph Theory ◽  
Network Analysis ◽  
Network Structure ◽  
Significant Influence ◽  
Female Students ◽  
Path Distance

The application of Graph Theory Concept in Communication Network Analysis is interesting to observe. This research was carried out to learn how Communication Network structure was formed and who had necessary role in the network. It was explorative research and conducted at Female Students’ Dormitory of State Islamic Institute of Palopo (Asrama Putri IAIN Palopo). The results were interpreted by using Microsoft NodeXL Version 1.0.1.113. It was found that the communication network structure of female students’ who stayed at the Dormitory decentralized. It shows that each student had same opportunity to communicate one another directly or indirectly, which 4 to 9 path distance. It was also identified that from 110 people, Suarni was the student who had significant influence in the communication network.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

scholarly journals Tight knit under stress: colony resilience to the loss of tandem leaders during relocation in an Indian ant

2015 ◽  
Vol 2 (9) ◽  
pp. 150104 ◽  
Author(s):  
Swetashree Kolay ◽  
Sumana Annagiri
Keyword(s):  
Network Analysis ◽  
Social Insects ◽  
Network Structure ◽  
Similar Rate ◽  
Tandem Running ◽  
Frequent Event ◽  
Random Removal ◽  
Task Organization

The movement of colonies from one nest to another is a frequent event in the lives of many social insects and is important for their survival and propagation. This goal-oriented task is accomplished by means of tandem running in some ant species, such as Diacamma indicum . Tandem leaders are central to this process as they know the location of the new nest and lead colony members to it. Relocations involving targeted removal of leaders were compared with unmanipulated and random member removal relocations. Behavioural observations were integrated with network analysis to examine the differences in the pattern of task organization at the level of individuals and that of the colony. All colonies completed relocation successfully and leaders who substituted the removed tandem leaders conducted the task at a similar rate having redistributed the task in a less skewed manner. In terms of network structure, this resilience was due to significantly higher density and outcloseness indicating increased interaction between substitute leaders. By contrast, leader–follower interactions and random removal networks showed no discernible changes. Similar explorations of other goal-oriented tasks in other societies will possibly unveil new facets in the interplay between individuals that enable the group to respond effectively to stress.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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.

scholarly journals Network Analysis for Economics and Finance: An Application to Firm Ownership

2021 ◽  
pp. 331-355
Author(s):  
Janina Engel ◽  
Michela Nardo ◽  
Michela Rancan
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Giant Component ◽  
Model Data ◽  
Firm Ownership ◽  
Network Metrics ◽  
Different Levels ◽  
Significant Concentration

AbstractIn this chapter, we introduce network analysis as an approach to model data in economics and finance. First, we review the most recent empirical applications using network analysis in economics and finance. Second, we introduce the main network metrics that are useful to describe the overall network structure and characterize the position of a specific node in the network. Third, we model information on firm ownership as a network: firms are the nodes while ownership relationships are the linkages. Data are retrieved from Orbis including information of millions of firms and their shareholders at worldwide level. We describe the necessary steps to construct the highly complex international ownership network. We then analyze its structure and compute the main metrics. We find that it forms a giant component with a significant number of nodes connected to each other. Network statistics show that a limited number of shareholders control many firms, revealing a significant concentration of power. Finally, we show how these measures computed at different levels of granularity (i.e., sector of activity) can provide useful policy insights.

Community detection in complex networks by using membrane algorithm

2018 ◽  
Vol 29 (01) ◽  
pp. 1850003 ◽  
Author(s):  
Chuang Liu ◽  
Linan Fan ◽  
Zhou Liu ◽  
Xiang Dai ◽  
Jiamei Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Community Detection ◽  
Membrane Structure ◽  
Superior Performance ◽  
Membrane Systems ◽  
Membrane Algorithm ◽  
Differential Evolutionary Algorithm ◽  
Network Modularity

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

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