scholarly journals Node Similarity Measure in Directed Weighted Complex Network Based on Node Nearest Neighbor Local Network Relative Weighted Entropy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 32432-32441
Author(s):  
Wanchang Jiang ◽  
Yinghui Wang
Keyword(s):  
Complex Network ◽  
Similarity Measure ◽  
Nearest Neighbor ◽  
Local Network ◽  
Node Similarity ◽  
Weighted Complex Network ◽  
Weighted Entropy

Time-space evolution of the goods export network of the Belt and Road countries: The view of weighted complex network

2021 ◽  
pp. 1-10
Author(s):  
Min Liu ◽  
Weixian Xue ◽  
Lisong He ◽  
Xue Yan
Keyword(s):  
South Korea ◽  
Complex Network ◽  
Leading Edge ◽  
Structural Features ◽  
Asia Pacific ◽  
Trade Volume ◽  
Time Space ◽  
Belt And Road ◽  
Weighted Complex Network ◽  
The Belt And Road

The weighted complex network is utilized to analyze the evolution of the overall structural features of the goods export network and the role transitions of each country in the network. The research suggests: 1. The network of exports of the Belt and Road countries has transformed from multi-core pattern into one extreme along with multi-core pattern; 2. China, South Korea, Russia, Singapore and Italy are the highest-ranking countries in the network. Among these countries, the influence of China is on the rise, South Korea South Korea’s influence remains basically unchanged., however, Russia, Singapore and Italy are on the decline; 3. The leading edge of Asia-Pacific block in the network has been enhanced year by year. Not only has the trade volume within the block increased to 50% of the whole network, but the trade export to other three blocks has significant increasement. The total volume of trade in European block increased greatly and its block mode has transformed from external to universal. The trade volume of the former Soviet Union block along with the West Asia-Africa block increased significantly as well, but there is still a large gap compared with the European block and Asia-Pacific block.

A novel algorithm for community detection based on resistance distance and similarity

2021 ◽  
pp. 2150164
Author(s):  
Pengli Lu ◽  
Zhou Yu ◽  
Yuhong Guo
Keyword(s):  
Community Detection ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Detection Algorithm ◽  
Structure And Function ◽  
Resistance Distance ◽  
Node Similarity ◽  
The Common ◽  
Community Detection Algorithm ◽  
And Function

Community detection is important for understanding the structure and function of networks. Resistance distance is a kind of distance function inherent in the network itself, which has important applications in many fields. In this paper, we propose a novel community detection algorithm based on resistance distance and similarity. First, we propose the node similarity, which is based on the common nodes and resistance distance. Then, we define the distance function between nodes by similarity. Furthermore, we calculate the distance between communities by using the distance between nodes. Finally, we detect the community structure in the network according to the nearest-neighbor nodes being in the same community. Experimental results on artificial networks and real-world networks show that the proposed algorithm can effectively detect the community structures in complex networks.

Link prediction based on contribution of neighbors

2020 ◽  
Vol 31 (11) ◽  
pp. 2050158
Author(s):  
Xiang-Chun Liu ◽  
Dian-Qing Meng ◽  
Xu-Zhen Zhu ◽  
Yang Tian
Keyword(s):  
Complex Network ◽  
Link Prediction ◽  
Node Degree ◽  
Prediction Methods ◽  
Obvious Advantage ◽  
Node Similarity ◽  
The Difference

Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.

scholarly journals Towards effective discovery of natural communities in complex networks and implications in e-commerce

2020 ◽  
Author(s):  
Swarup Chattopadhyay ◽  
Tanmay Basu ◽  
Asit K. Das ◽  
Kuntal Ghosh ◽  
Late C. A. Murthy
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Similarity Measure ◽  
Recommender System ◽  
Data Clustering ◽  
Ground Truth ◽  
Normalized Mutual Information ◽  
Detection Algorithms ◽  
Node Similarity ◽  
Art Community

AbstractAutomated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.

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