scholarly journals CINNA: An R/CRAN package to decipher Central Informative Nodes in Network Analysis

2017 ◽  
Author(s):  
Minoo Ashtiani ◽  
Mehdi Mirzaie ◽  
Mohieddin Jafari
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Dimensional Reduction ◽  
Network Science ◽  
The Other ◽  
Centrality Measures ◽  
Large Network ◽  
Topological Features ◽  
Reduction Methods ◽  
Centrality Analysis

AbstractIn network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us to sieve our large network and distinguish coarse vertices. By that, important decisions could be made based on the circumstances of these vertices on the overall behavior of networks. These vertices are potentially assumed as central or essential nodes. However, the centrality analysis has always been accompanied by a series of ambiguities, since there are a large number of well-known centrality measures, with different algorithms pointing to these essential nodes and there is no well-defined preference. Which measure explore more information in a given network about node essentiality according to the topological features? While here, we tried to provide a pipeline to have a comparison among all proper centrality measures regarding the network structure and choose the most informative one according to dimensional reduction methods. Central Informative Nodes in Network Analysis (CINNA) package is prepared to gather all required function for centrality analysis in the weighted/unweighted and directed/undirected networks.Availability and implementationCINNA is available in CRAN, including a tutorial. URL: https://cran.r-proiect.org/web/packages/CINNA/index.htmlContact:[email protected]

scholarly journals A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)

2021 ◽  
Vol 10 (7) ◽  
pp. 491
Author(s):  
Manuel Curado ◽  
Rocio Rodriguez ◽  
Manuel Jimenez ◽  
Leandro Tortosa ◽  
Jose F. Vicent
Keyword(s):  
Network Analysis ◽  
The Other ◽  
Centrality Measures ◽  
Innovative Behavior ◽  
Street Network ◽  
Commercial Activity ◽  
Determining Factors ◽  
Combine Network ◽  
The City

Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of the streets of a city using different accessibility parameters. We combine network structure and accessibility factors to explore the association between innovative behavior within the street network, and the relationships with the commercial activity in a city. Finally, we present a case study of the city of Avila, locating the most inaccessible areas of the city using centrality measures and analyzing the effects, in terms of accessibility, on the commerce and services of the city.

scholarly journals The Effect Of The Relationships Between Affiliated Firms On Direction Of Income Shifting Within Business Groups

2014 ◽  
Vol 30 (3) ◽  
pp. 817 ◽  
Author(s):  
Kyung Jin Park ◽  
Joohyun Lim ◽  
Ki Young Kim
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Ownership Structure ◽  
Business Group ◽  
The Other ◽  
Centrality Measures ◽  
Income Shifting ◽  
Tax Burden

<p>In this study, we examined how income shifting performs among affiliates in a business group to maximize the benefits of the entire business group in terms of minimizing the tax burden, with a particular focus on the direction of income shifting between affiliates within the business group. We find that tax-related decision-making for the entire business group is affected by the relationships between the affiliated firms, that is, the ownership structure of the whole business group. To analyze the ownership structure, we use centrality measures in a social network analysis. The results show that affiliates with the higher outdegree-centrality; that is, firms investing more shareholdings in other affiliates have a tendency to perform more income shifting. On the other hand, the affiliates with high indegree-centrality, that is, firms which are owned by other affiliates, were revealed to be given the income shifting from other affiliated firms to minimize the tax burden of the entire business group.</p>

Core Kernels for Complex Network Analysis

2018 ◽  
pp. 30-58
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Network Science ◽  
Efficient Algorithms ◽  
The Other ◽  
Software Systems ◽  
Layer By Layer ◽  
Graph Visualization ◽  
Graph Problems ◽  
Centrality Metrics

The emergence of Network Science has motivated a renewed interest in classical graph problems for the analysis of the topology of complex networks. For example, important centrality metrics, such as the betweenness, the stress, the eccentricity, and the closeness centralities, are all based on BFS. On the other hand, the k-core decomposition of graphs defines a hierarchy of internal cores and decomposes large networks layer by layer. The k-core decomposition has been successfully applied in a variety of domains, including large graph visualization and fingerprinting, analysis of large software systems, and fraud detection. In this chapter, the authors review known efficient algorithms for traversing and decomposing large complex networks and provide insights on how the decomposition of graphs in k-cores can be useful for developing novel topology-aware algorithms.

How Members of Covert Networks Conceal the Identities of Their Leaders

2022 ◽  
Vol 13 (1) ◽  
pp. 1-29
Author(s):  
Marcin Waniek ◽  
Tomasz P. Michalak ◽  
Michael Wooldridge ◽  
Talal Rahwan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Centrality Measures ◽  
Analysis Tools ◽  
Np Complete ◽  
Covert Networks

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.

Street network analysis “edge effects”: Examining the sensitivity of centrality measures to boundary conditions

2016 ◽  
Vol 44 (5) ◽  
pp. 819-836 ◽  
Author(s):  
Jorge Gil
Keyword(s):  
Network Analysis ◽  
Edge Effect ◽  
Research Question ◽  
Network Models ◽  
Centrality Measures ◽  
Street Network ◽  
Regional Environment ◽  
Street Networks ◽  
Centrality Analysis ◽  
The Impact

With increased interest in the use of network analysis to study the urban and regional environment, it is important to understand the sensitivity of centrality analysis results to the so-called “edge effect”. Most street network models have artificial boundaries, and there are principles that can be applied to minimise or eliminate the effect of the boundary condition. However, the extent of this impact has not been systematically studied and remains little understood. In this article we present an empirical study on the impact of different network model boundaries on the results of closeness and betweenness centrality analysis of street networks. The results demonstrate that the centrality measures are affected differently by the edge effect, and that the same centrality measure is affected differently depending on the type of network distance used. These results highlight the importance, in any study of street networks, of defining the network's boundary in a way that is relevant to the research question, and of selecting appropriate analysis parameters and statistics.

scholarly journals An examination of transaction interdependency: a perspective in the animal health and nutrition industrial system

2014 ◽  
Vol 14 (1) ◽  
pp. 43-57
Author(s):  
K.D. Harris ◽  
H.S. James Jr.
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Animal Health ◽  
The Other ◽  
Business Networks ◽  
Industrial System ◽  
Material Flows ◽  
Health And Nutrition ◽  
Economic Exchanges ◽  
Technical Material

The research examining bioscience networks has been studied from two perspectives. One view comes from economics and the other sociology. We examine the technical (material flows) and people aspects (information sharing) of interdependency in the context of economic exchanges in a bioscience network. The empirical contributions are the techniques used to explain the network structure of a burgeoning animal health and nutrition bioscience network and the portability of network analysis concepts that provides the potential to manage diverse business networks. The results suggest the economic exchanges can be traced back to the underlying interactions that safeguard transactions and influence the flow of resources and information.

scholarly journals Football is becoming more predictable; network analysis of 88 thousand matches in 11 major leagues

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Victor Martins Maimone ◽  
Taha Yasseri
Keyword(s):  
Network Analysis ◽  
Network Science ◽  
Direct Analysis ◽  
High Income ◽  
The Other ◽  
Science Approach ◽  
Home Field Advantage ◽  
The One ◽  
Over Time

In recent years, excessive monetization of football and professionalism among the players have been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work, we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football’s predictability or therefore, lack of excitement; however, we propose several hypotheses which could be tested in future analyses.

scholarly journals NETWORK ANALYSIS OF THE STACK OVERFLOW TAGS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 195-202
Author(s):  
Z. Edrees
Keyword(s):  
Network Analysis ◽  
Degree Distribution ◽  
Network Science ◽  
Centrality Measures ◽  
Node Degree ◽  
Stack Overflow ◽  
Node Importance ◽  
Node Degree Distribution

Abstract. In this paper we made analysis for the stack overflow tags by using different criteria's in network science, one of the advantages of network analysis is that complex of connections can be made cleared, we started this work in first step by extracted data from dataset after that applied network concepts node degree distribution, node importance (centrality measures), also we provided a brief demonstration of how we can use graph network and tools to analyze semi-structured text as (Tags).

Use of Social Network Analysis in Telecommunication Domain

2018 ◽  
pp. 152-178 ◽  
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy ◽  
Monalisa Mishra ◽  
Bijayalaxmi Panda
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Other ◽  
Centrality Measures ◽  
Customer Data ◽  
Relationship Types ◽  
Heterogeneous Social Networks

Social network analysis (SNA) is the analysis of social communication through network and graph theory. In our chapter the application of SNA has been explored in telecommunication domain. Telecom data consist of Customer data and Call Detail Data (CDR). The proposed work, considers the attributes of call detail data and customer data as different relationship types to model our Multi-relational Telecommunication social network. Typical work on social network analysis includes the discovery of group of customers who shares similar properties. A new challenge is the mining of hidden communities on such heterogeneous social networks, to group the customers as churners and non-churners in Telecommunication social network. After the analysis of the available data we constructed a Weights Multi-relational Social Network, in which each relation carry a different weight, representing how close two customers are with one another. The centrality measures depict the intensity of the customer closeness, hence we can determine the customer who influence the other customer to churn.

Identifying key players in bipartite networks

2020 ◽  
Vol 8 (1) ◽  
pp. 42-61
Author(s):  
Scott W. Duxbury
Keyword(s):  
Network Structure ◽  
The Other ◽  
Centrality Measures ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Key Players ◽  
Two Measures ◽  
The One

AbstractMeasures of bipartite network structure have recently gained attention from network scholars. However, there is currently no measure for identifying key players in two-mode networks. This article proposes measures for identifying key players in bipartite networks. It focuses on two measures: fragmentation and cohesion centrality. It extends the centrality measures to bipartite networks by considering (1) cohesion and fragmentation centrality within a one-mode projection, (2) cross-modal cohesion and fragmentation centrality, where a node in one mode is influential in the one-mode projection of the other mode, and (3) cohesion and fragmentation centrality across the entire bipartite structure. Empirical examples are provided for the Southern Women’s data and on the Ndrangheta mafia data.

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