Hypergraphs,which encapsulate interactions of higher-order beyond mere pairwise connections,are essential for representing polyadic relationships within complex systems.Consequently,an increasing number of researchers...Hypergraphs,which encapsulate interactions of higher-order beyond mere pairwise connections,are essential for representing polyadic relationships within complex systems.Consequently,an increasing number of researchers are focusing on the centrality problem in hypergraphs.Specifically,researchers are tackling the challenge of utilizing higher-order structures to effectively define centrality metrics.This paper presents a novel approach,LGK,derived from the K-shell decomposition method,which incorporates both global and local perspectives.Empirical evaluations indicate that the LGK method provides several advantages,including reduced time complexity and improved accuracy in identifying critical nodes in hypergraphs.展开更多
Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well a...Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.展开更多
1 Introduction Vital nodes refer to specific nodes within a network that can exert a greater influence on the structure and functionality of the network compared to other nodes[1].During the past decades,people mainly...1 Introduction Vital nodes refer to specific nodes within a network that can exert a greater influence on the structure and functionality of the network compared to other nodes[1].During the past decades,people mainly focus on developing new vital node identification algorithms.However,there is still no consensus on how to evaluate and compare these methods.This is mainly due to the lack of standard benchmark networks with known ground-truth vital nodes.展开更多
Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important fa...Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important factors such as interactivity and transitivity between multiple nodes.To identify the vital nodes more accurately,a high-order structure,named as the motif,is introduced in this paper as the basic unit to evaluate the similarity among the node in the complex network.It proposes a notion of high-order degree of nodes in complex network and fused the effect of the high-order structure and the lower-order structure of nodes,using evidence theory to determine the vital nodes more efficiently and accurately.The algorithm was evaluated from the function of network structure.And the SIR model was adopted to examine the spreading influence of the nodes ranked.The results of experiments in different datasets demonstrate that the algorithm designed can identify vital nodes in the social network accurately.展开更多
现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结...现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结合复杂网络微观结构和宏观结构构造节点特征;其次,针对特征冗余问题,提出一个融合选择性状态空间模型(State Space Models)和自监督学习的节点特征提取方法;最后,针对泛化性低问题,利用图结构学习在模型训练层面优化损失函数提高分类精度.利用4个公开数据集上进行了广泛实验,本文方法优于次优方法4.66%,节点分辨率保持稳定.实验表明,所提出方法能有效的识别不同网络的关键节点.展开更多
文摘Hypergraphs,which encapsulate interactions of higher-order beyond mere pairwise connections,are essential for representing polyadic relationships within complex systems.Consequently,an increasing number of researchers are focusing on the centrality problem in hypergraphs.Specifically,researchers are tackling the challenge of utilizing higher-order structures to effectively define centrality metrics.This paper presents a novel approach,LGK,derived from the K-shell decomposition method,which incorporates both global and local perspectives.Empirical evaluations indicate that the LGK method provides several advantages,including reduced time complexity and improved accuracy in identifying critical nodes in hypergraphs.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61672124,61370145,61173183,and 61503375)the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund,China(Grant No.MMJJ20170203)
文摘Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.62472064).
文摘1 Introduction Vital nodes refer to specific nodes within a network that can exert a greater influence on the structure and functionality of the network compared to other nodes[1].During the past decades,people mainly focus on developing new vital node identification algorithms.However,there is still no consensus on how to evaluate and compare these methods.This is mainly due to the lack of standard benchmark networks with known ground-truth vital nodes.
基金the Natural Science Foundation of China(No.61662066,61163010).
文摘Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important factors such as interactivity and transitivity between multiple nodes.To identify the vital nodes more accurately,a high-order structure,named as the motif,is introduced in this paper as the basic unit to evaluate the similarity among the node in the complex network.It proposes a notion of high-order degree of nodes in complex network and fused the effect of the high-order structure and the lower-order structure of nodes,using evidence theory to determine the vital nodes more efficiently and accurately.The algorithm was evaluated from the function of network structure.And the SIR model was adopted to examine the spreading influence of the nodes ranked.The results of experiments in different datasets demonstrate that the algorithm designed can identify vital nodes in the social network accurately.
文摘现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结合复杂网络微观结构和宏观结构构造节点特征;其次,针对特征冗余问题,提出一个融合选择性状态空间模型(State Space Models)和自监督学习的节点特征提取方法;最后,针对泛化性低问题,利用图结构学习在模型训练层面优化损失函数提高分类精度.利用4个公开数据集上进行了广泛实验,本文方法优于次优方法4.66%,节点分辨率保持稳定.实验表明,所提出方法能有效的识别不同网络的关键节点.
