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The real-world network is heterogeneous, and it is an important and challenging task to effectively identify the influential nodes in complex networks. Identification of influential nodes is widely used in social, biological, transportation, information and other networks with complex structures to help us solve a variety of complex problems. In recent years, the identification of influence nodes has received a lot of attention, and scholars have proposed various methods based on different practical problems. This paper proposes a new method to identify influential nodes, namely Attraction based on Node and Community (ANC). By considering the attraction of nodes to nodes and nodes to community structure, this method quantifies the attraction of a node, and the attraction of a node is used to represent its influence. To illustrate the effectiveness of ANC, we did extensive experiments on six real-world networks and the results show that the ANC algorithm is superior to the representative algorithms in terms of the accuracy and has lower time complexity as well. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, the social network has gradually become an indispensable platform for users to release information, obtain information, and share information. Users are not only receivers of information, but also publishers and disseminators of information. How to select a certain number of users to use their influence to achieve the maximum dissemination of information has become a hot topic at home and abroad. Rapid and accurate identification of influential nodes in the network is of great practical significance, such as the rapid dissemination, suppression of social network information, and the smooth operation of the network. Therefore, from the perspective of improving computational accuracy and efficiency, we propose an influential node identification method based on effective distance, named KDEC. By quantifying the effective distance between nodes and combining the position of the node in the network and its local structure, the influence of the node in the network is obtained, which is used as an indicator to evaluate the influence of the node. Through experimental analysis of a lot of real-world networks, the results show that the method can quickly and accurately identify the influential nodes in the network, and is better than some classical algorithms and some recently proposed algorithms. [ABSTRACT FROM AUTHOR]

Identifying influential nodes in complex networks is still an open issue. A number of measures have been proposed to improve the validity and accuracy of the influential nodes in complex networks. In this paper, we propose a new method, called GLS, to identify influential nodes. This method aims to determine the influence of the nodes themselves, while combining the structural characteristics of the network. This method considers not only the local structure of the network but also its global structure. The influence of the global structure is measured by its closeness to all other nodes in the network, but the influence of local structures only considers the influence contribution of the nearest neighbor nodes. To evaluate the performance of GLS, we use the Susceptible-Infected-Recovered (SIR) model to examine the spreading efficiency of each node, and compare GLS with PageRank, Hyperlink Induced Topic Search (Hits), K-shell, H-index, eigenvector centrality (EC), closeness centrality (CC), ProfitLeader, betweenness centrality (BC) and Weighted Formal Concept Analysis (WFCA) on 8 real-world networks. The experimental results show that GLS can rank the spreading ability of nodes more accurately and more efficiently than other methods. • Intuitive and effective influential nodes detection: We consider the detection of influential nodes from a new perspective, combining the global and local structure of the network. It reflects the internal relationship and the information interaction ability between nodes. • Parameter-free: The GLS method does not rely on prior knowledge and parameter adjustments, and it can automatically identify the influential nodes. • High performance: Compared to several typically used methods for the identification of influential nodes, the GLS method is shown to be more effective. [ABSTRACT FROM AUTHOR]