Identifying influential nodes based on fuzzy local dimension in complex networks

Wen, Tao; Jiang, Wen

doi:10.1016/j.chaos.2019.01.011

Cited by 49 publications

(18 citation statements)

References 93 publications

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“…Its noticeable advantage is that its time complexity is considerably low. Wen et al [40] propose a new fuzzy local dimension to rank the influential nodes in complex networks. The method utilizes fuzzy sets to consider the influence of the distance from the core node on the local dimension of the core node to change the influence ability, only concentrating on individuals in complex networks, not the entire global structure.…”

Section: Identification Of the Influential Nodes In Graph Neural Networkmentioning

confidence: 99%

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Wang

Zhuang

Xie

2021

Neural Process Lett

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Accurately tracking the influential nodes in swarm is the key to improve the control or defense against the drone combat. But the identification of influential nodes is intractable due to hardly capturing the pattern information of the running drones based on the traditional Graph Theory. To tackle this issue, in this paper, we propose a novel framework to identify the influential nodes, which includes Latent Interaction Graph Extraction (LIGE), Degree Centrality based Influential Inference (DCII) and Formation Stability Coefficient based Re-ranking (FSCR). LIGE is employed to reconstruct the latent interaction graph from the dynamic trajectory data by exploiting the encoder in Neural Relational Inference. DCII invokes the traditional graph model and measures the significance of nodes. In the re-ranking module FSCR, we introduce a novel metric, the formation stability coefficient, to measure swarm formation stability and re-ranking the candidates. The experimental results on the Drone swarm datasets demonstrate that our framework can effectively identify the influential nodes of drone swarm. The extracting time in LIGE is relatively long, which makes it difficult to recognize the core node within the limit time. By optimizing the encoder structure, the extraction time can be reduced to at least twice the original.

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Section: Identification Of the Influential Nodes In Graph Neural Networkmentioning

confidence: 99%

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Wang

Zhuang

Xie

2021

Neural Process Lett

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“…The experiment was conducted on eight real-world networks, Jazz, NS [48], GrQc, Email, EEC, Facebook [41], PB [49] and USAir [12], including two communication networks (Email, EEC), one transportation network (USAir), two social networks (Facebook, PB) and three cooperative networks (Jazz, NS, GrQc). Other relevant information about the network is displayed on Table III.…”

Section: Datasetsmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

et al. 2019

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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.

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“…The existing studies on complex network information mining are generally ranked on the basis of the importance of all nodes and edges in the network [6][7][8][9][10]. However, determining which nodes are the most important in the network relative to one or one group of specific nodes presents an issue.…”

Section: Introductionmentioning

confidence: 99%

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

et al. 2022

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In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

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Identifying influential nodes based on fuzzy local dimension in complex networks

Cited by 49 publications

References 93 publications

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

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