A New Method of Identifying Influential Nodes in Complex Network,- Diffusion Centrality

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Complex Network ◽  
New Method ◽  
Influential Nodes ◽  
Network Diffusion

Evaluation of invulnerability of scale-free networks using total information of local sub-graph

2018 ◽  
Vol 29 (08) ◽  
pp. 1850075
Author(s):  
Tingyuan Nie ◽  
Xinling Guo ◽  
Mengda Lin ◽  
Kun Zhao
Keyword(s):  
Mutual Information ◽  
Complex Network ◽  
Fundamental Problem ◽  
The Self ◽  
Practical Significance ◽  
Scale Free ◽  
Total Information ◽  
Scale Free Networks ◽  
Influential Nodes ◽  
Definition Of

The quantification for the invulnerability of complex network is a fundamental problem in which identifying influential nodes is of theoretical and practical significance. In this paper, we propose a novel definition of centrality named total information (TC) which derives from a local sub-graph being constructed by a node and its neighbors. The centrality is then defined as the sum of the self-information of the node and the mutual information of its neighbor nodes. We use the proposed centrality to identify the importance of nodes through the evaluation of the invulnerability of scale-free networks. It shows both the efficiency and the effectiveness of the proposed centrality are improved, compared with traditional centralities.

Community centrality for node's influential ranking in complex network

2014 ◽  
Vol 25 (03) ◽  
pp. 1350096 ◽  
Author(s):  
Biao Cai ◽  
Xian-Guo Tuo ◽  
Kai-Xue Yang ◽  
Ming-Zhe Liu
Keyword(s):  
Complex Network ◽  
Spanning Tree ◽  
Time Complexity ◽  
Local Community ◽  
Sir Model ◽  
Minimal Spanning Tree ◽  
Path Computation ◽  
Influential Nodes ◽  
Shortest Path Computation ◽  
Very High

Some tiny party of influential nodes may highly affect spread of information in complex networks. For the case of very high time complexity in the shortest path computation of global centralities, making use of local community centrality to identify influential nodes is an open and possible problem. Compared to degree and local centralities, a five-heartbeat forward community centrality is proposed in this paper, in which a five-step induced sub-graph of certain node in the network will be achieved. Next, we induce the minimal spanning tree (MMT) of the sub-graph. Finally, we take the sum of all weights of the MMT as community centrality measurement that needs to be the influential ranking of the node. We use the susceptible, infected and recovered (SIR) model to evaluate the performance of this method on several public test network data and explore the forward steps of community centrality by experiments. Simulative results show that our method with five steps can identify the influential ranking of nodes in complex network as well.

scholarly journals Identifying Influential Nodes of Complex Networks Based on Trust-Value

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 280
Author(s):  
Jinfang Sheng ◽  
Jiafu Zhu ◽  
Yayun Wang ◽  
Bin Wang ◽  
Zheng’ang Hou
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Trust Value ◽  
The Real ◽  
The World ◽  
Attribute Information ◽  
Similarity Ratio ◽  
Influential Nodes ◽  
Influential Node

The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.

scholarly journals Controlling COVID-19 Propagation with Quarantine of Influential Nodes

2021 ◽  
Author(s):  
Sarkhosh S. Chaharborj ◽  
Shahriar S. Chaharborj ◽  
Phang Pei See
Keyword(s):  
Complex Network ◽  
Structural Organization ◽  
Centrality Measures ◽  
Topsis Method ◽  
Epidemic Disease ◽  
Dependability Analysis ◽  
Order Preference ◽  
Si Model ◽  
Influential Nodes ◽  
Important Nodes

Abstract We study importance of influential nodes in spreading of epidemic COVID-19 in a complex network. We will show that quarantine of important and influential nodes or consider of health protocols by efficient nodes is very helpful and effective in the controlling of spreading epidemic COVID-19 in a complex network. Therefore, identifying influential nodes in complex networks is the very significant part of dependability analysis, which has been a clue matter in analyzing the structural organization of a network. The important nodes can be considered as a person or as an organization. To find the influential nodes we use the technique for order preference by similarity to ideal solution (TOPSIS) method with new proposed formula to obtain the efficient weights. We use various centrality measures as the multi-attribute of complex network in the TOPSIS method. We define a formula for spreading probability of epidemic disease in a complex network to study the power of infection spreading with quarantine of important nodes. In the following, we use the Susceptible–Infected (SI) model to figure out the performance and efficiency of the proposed methods. The proposed method has been examined for efficiency and practicality using numerical examples.

scholarly journals Identifying Key Bus Stations Based on Complex Network Theory considering the Hybrid Influence and Passenger Flow: A Case Study of Beijing, China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianlin Jia ◽  
Yanyan Chen ◽  
Ning Chen ◽  
Hui Yao ◽  
Yongxing Li ◽  
...  
Keyword(s):  
Complex Network ◽  
Shortest Paths ◽  
Response Speed ◽  
New Method ◽  
Transfer Lines ◽  
Complex Network Theory ◽  
Passenger Flow ◽  
Network Methods ◽  
Bus Network ◽  
Beijing China

In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed.

A new method for Community detection in complex network on the basis of Similarity

2020 ◽  
Vol 13 ◽  
Author(s):  
Munawar Hussain ◽  
Awais Akram
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Evaluation Method ◽  
Structural Similarity ◽  
New Method ◽  
Edge Removal ◽  
Unweighted Network ◽  
Novel Method ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Introduction: Regarding complex network, to find optimal communities in the network has become a key topic in the field of network theory. It is crucial to understand the Structure and functionality of associated networks. In this paper, we propose a new method of community detection that works on the structural similarity of a network (SSN). Method: This method works in two steps, at the first step, it removes edges between the different groups of nodes which are not very similar to each other. As a result of edge removal, the network is divided into many small random communities, which are referred as main communities. Result: In the second step, we apply the evaluation method (EM), it chooses the best quality communities, from all main communities which already produced at the first step. At last, we apply evaluation metrics to our proposed method and benchmarking methods, which show that the SSN method can detect comparatively more accurate results than other methods in this paper. Conclusion: In this article, we proposed a novel method for community detection in networks, called structural similarity of network (SSN). It works in two steps. In the first step, it randomly removes low similarity edges from the network, which makes several small disconnected communities, called as main communities. Afterward, the main communities are merged to search for the final communities, which are near to actual existing communities of the network. Discussion: This approach is defined on the base of the unweighted network, so in Further research it could be used on weighted networks and can explore some new deep-down attributes. Furthermore, it will be used Facebook and twitter weighted data with the artificial intelligence approach.

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