scholarly journals Efficient algorithms for estimating loss of information in a complex network: Applications to intentional risk analysis

2015 ◽  
Vol 10 (1) ◽  
pp. 195-208 ◽  
Author(s):  
Ángel Pérez ◽  
Regino Criado ◽  
Victor Chapela ◽  
Santiago Moral ◽  
Miguel Romance
Keyword(s):  
Risk Analysis ◽  
Complex Network ◽  
Efficient Algorithms ◽  
Loss Of Information ◽  
Network Applications

The Risk Analysis of International Stock Market Based on Complex Network Approach

2021 ◽  
Author(s):  
Yingli Wang ◽  
Yan Wang ◽  
Yang Liu ◽  
Junling Cai
Keyword(s):  
Risk Analysis ◽  
Stock Market ◽  
Complex Network ◽  
Network Approach

m-PageRank: A NOVEL CENTRALITY MEASURE FOR MULTILAYER NETWORKS

2020 ◽  
Vol 23 (05) ◽  
pp. 2050012
Author(s):  
JO CHERIYAN ◽  
G. P. SAJEEV
Keyword(s):  
Social Network ◽  
Complex Network ◽  
Biological Networks ◽  
Computational Cost ◽  
Business Networks ◽  
Multilayer Networks ◽  
Network Applications ◽  
The Social ◽  
Socially Relevant ◽  
High Computational Cost

A complex network is an explicit model for a real-world system such as technological networks, social networks, business networks, and biological networks. The social network is an Internet-based media for socially relevant activities like stay connected with families and friends, colleagues, and customers, for socializing, business, or both. The key nodes, usually called central nodes, capable of measuring the performance of various social network applications. Identifying influencing nodes is primary research for any network analysis research. Degree centrality, a locally computed metric, is simple to compute but not persuasive. The global metrics like betweenness centrality and PageRank are only useful for the systems with a simple structure, but incur a high computational cost with the addition of layers. This paper proposes a novel metric m-PageRank for ranking nodes in a multi-layer complex network. The m-PageRank is an advancement of PageRank. It integrates the existence of the rank of each layer from where the connection connects. The proposed metric was validated through simulations performed over various multilayer networks. The result shows that m-PageRank computes the rank of each node accurately. We observe that the comparison with state-of-the-art metrics demonstrated the suitability of the proposed metric.

Application and Discussion of Cloud Computing in Academic Libraries

2015 ◽  
Vol 719-720 ◽  
pp. 929-934
Author(s):  
Chun Hong Zhang
Keyword(s):  
Cloud Computing ◽  
Complex Network ◽  
Data Storage ◽  
Resource Sharing ◽  
Full Range ◽  
Computing Technology ◽  
Digital Resources ◽  
Network Applications ◽  
One Stop ◽  
Interactive Services

Beginning with the meaning and characteristics of cloud computing, author discusses the library is integrated with cloud computing technology. Cloud computing plays a very important role in resource development and services for the library. Cloud computing provide the sharing of hardware and software on the data storage, data management, it also has the capabilities with the database resource sharing, unified digital resources, one-stop entrance, a full range of user interactive services, the expansion of the complex network applications and services. This paper proposes several suggestions about the characteristics of cloud computing technology in library applications.

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Risk Analysis ◽  
2022 ◽  
Author(s):  
Xiaoxue Ma ◽  
Wanyi Deng ◽  
Weiliang Qiao ◽  
Huiwen Luo
Keyword(s):  
Risk Analysis ◽  
Complex Network

scholarly journals Flow approaches to community allocation in complex network systems

2021 ◽  
pp. 122-127
Author(s):  
Olexandr Polishchuk
Keyword(s):  
Complex Network ◽  
Efficient Algorithms ◽  
Network System ◽  
Visual Methods ◽  
Flow Core ◽  
Network Systems

The paper investigates the problem of finding communities in complex network systems, the detec-tion of which allows a better understanding of the laws of their functioning. To solve this problem, two approaches are proposed based on the use of flows characteristics of complex network. The first of these approaches consists in calculating the parameters of influence of separate subsys-tems of the network system, distinguished by the principles of ordering or subordination, and the second, in using the concept of its flow core. Based on the proposed approaches, reliable criteria for finding communities have been formulated and efficient algorithms for their detection in com-plex network systems have been developed. It is shown that the proposed approaches make it pos-sible to single out communities in cases in which the existing numerical and visual methods turn out to be disabled.

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