Abstract
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.
Highlights
The Graph represents various real-world networks through points and lines, which has become a universal method to study complex networks
Aiming at the above problems, this paper proposes an influential node identification method based on effective distance, which uses the node’s attributes and interaction with neighboring nodes to comprehensively evaluate the importance of nodes in the network [14,15]
The main contributions of this work are as follows: (1) Accurate influential node detection: The KDEC method comprehensively considers a variety of attributes to sort the nodes, which can detect the influential nodes more effectively and obtain more accurate sorting results; (2) Parameter free: KDEC does not rely on prior knowledge and parameter adjustments but can automatically identify influential nodes; (3) Scalability: KDEC uses two-stage neighbors of nodes to identify influential nodes, which greatly reduces the calculation cost, and has no strict requirements on the global topology of the network
Summary
The Graph represents various real-world networks through points and lines, which has become a universal method to study complex networks. The main contributions of this work are as follows: (1) Accurate influential node detection: The KDEC method comprehensively considers a variety of attributes to sort the nodes, which can detect the influential nodes more effectively and obtain more accurate sorting results; (2) Parameter free: KDEC does not rely on prior knowledge and parameter adjustments but can automatically identify influential nodes; (3) Scalability: KDEC uses two-stage neighbors of nodes to identify influential nodes, which greatly reduces the calculation cost, and has no strict requirements on the global topology of the network It is suitable for connected or disconnected networks.
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