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1.
李泽鹏  左杨  王宏宇 《电子学报》2016,44(12):2967-2974
度量社交网络节点影响力是社交网络结构分析的关键问题之一。目前研究社交网络节点影响力的方法主要有两大类:中心度方法和节点删除方法。前者主要通过度或最短路径等因素来判断节点的影响力,不考虑网络的连通性;后者通过节点删除后对网络结构的破坏程度来判断,计算复杂性很高,不适用于较大规模的社交网络。通过结合社交网络的局部连通度及节点间的最短路径,提出了连通中心度来度量社交网络中节点的影响力,并给出了连通中心度的计算方法和一些特殊网络中节点的连通中心度的值。最后,通过实验说明该指标能很好地度量社交网络中节点的影响力。  相似文献   

2.
李旭杰  吉普  孙颖  李浩天  徐宁 《电讯技术》2024,(10):1644-1652
针对当前节点影响力评估算法准确度较低的情况,提出了一种基于深度强化学习的节点影响力排序算法。该算法从网络拆解的视角看待节点影响力,将节点影响力的排序问题转换为网络拆除策略的优化问题。算法首先利用排序学习训练图神经网络模型的节点特征提取能力,然后使用强化学习对依赖于网络状态的节点断连行为做价值学习,最后使用训练完成的模型预测网络拆除的最佳策略,即节点影响力的最准确排序。仿真实验证明,所提算法在典型真实数据集的CN(Crtical Node)与ND(Network Dismantling)问题上,相较于PageRank算法,准确度分别提升了31.1%与29.0%。同时,该算法具有较低的复杂度,可为网络稳定性分析和网络性能优化提供技术支撑。  相似文献   

3.
刘露  胡封晔  牛亮  彭涛 《电子学报》2019,47(9):1929-1936
异质网络相似度学习,即分析两个不同类型对象间的相关程度.不同类型对象在异质网络中的重要程度不同,它们在相似度学习过程中的发挥的作用也不同.针对异质网络,提出了一种基于节点影响力的相似度度量方法NISim,该模型既考虑了网络中的链接结构,也保留了网络中的语义信息,同时区分不同类型节点对异质网络的作用.在异质信息网络环境下,通过启发式规则区分并量化不同类型节点的影响力权值,并结合网络链接结构和节点间语义关系,解决了提高相似度学习准确性的问题.实验结果表明,该方法能够有效地对异质信息网络不同类型节点进行相似度度量,可以应用在网络搜索、推荐系统以及知识图谱构建等不同领域.  相似文献   

4.
在复杂网络研究中,精确辨识网络内的关键节点对于深入把握网络的结构特性和功能机制,以及增强复杂网络运行的稳固性和安全性具有尤为重要的作用。传统的K-shell方法仅依据节点在网络中的位置信息,排序结果太粗粒化,使得节点的区分度不大;仅考虑剩余度的影响,默认同层节点的外层节点数相同,这限制了评估结果的精确性和分辨力。为了解决这一问题,文中提出一种新的关键节点识别方法,该方法在原始K-shell算法思想之上综合考虑了局部影响力,补充了邻居节点和次邻居节点对所识别节点重要性的影响。首先,通过K-shell算法确定节点全局影响力,计算每个节点的Ks值;其次,通过度中心性算法确定所识别节点的邻居节点的影响力,而次邻居节点的影响力则通过其影响系数与数量的乘积来表征;最后,通过综合考虑邻居节点以及次邻居节点的作用来评估节点的局部影响力。具体而言,邻居节点的影响力通过其度中心性来量化,次邻居节点的影响力则由其影响系数与数量的乘积来表征。以相关性、单调性以及鲁棒性为评价标准,将文中方法在6个真实网络上进行验证,验证结果显示,提出的方法与目前主流方法相比,能更高效、准确地识别复杂网络中的关键节点,并具有较高的分辨率和准确性。  相似文献   

5.
复杂网络中的关键节点识别是研究复杂网络结构、功能、性质的重要基础,在市场营销、谣言控制、交通规划等不同领域都有很强的应用价值。节点的关键性等价于节点的影响力,因此,关键节点识别问题可看作节点影响力评估问题。文章提出了一种基于局部传播路径的复杂网络关键节点识别方法,该方法仅需计算目标节点两步之内的拓扑结构,还综合考虑了传播概率对节点影响力评估的影响。与常见的度中心性、介数中心性、接近中心性、Kshell中心性相比,该算法识别结果更准确,在不同传播概率下表现更稳定。  相似文献   

6.
《信息技术》2016,(5):19-21
复杂网络中的节点传播能力度量具有十分重要的意义,有助于抑制谣言扩散、疾病传播等。研究者们普遍用度量节点传播能力,然而度指标忽略了邻居节点的重要程度。文中考虑邻居节点度信息,提出了一种新的度量节点传播能力方法。该方法通过计算邻居节点度的α次方的和来度量节点的传播能力。通过α调整邻居节点对目标节点的重要程度,α越大,度大的邻居节点对目标节点的贡献越大。实验结果表明,在最优值α情况下,文中提出的算法相对于度和K-核分解方法能更加准确地度量节点的传播能力。文中提出的度量方法只考虑邻居节点度信息,因此非常适合于大规模网络的节点传播能力度量。  相似文献   

7.
一方面,社区信息沿着最短路径传播且随着传播逐渐衰减,信息传到较远位置可能性很小。另一方面,在信息量一定的情况下,在不同路径长度下,每条边累积信息量不同。由此两方面的考虑,引入节点影响力和局部中心度,结合GN算法删除最大边介数的核心思想,得到一种新的社区发现算法WLCD(weighted local community detection,WLCD)。实验证明,在三种真实网络数据集中,WLCD算法对比其他几种经典社区检测算法更好,在模块度、调整兰德系数、标准互信息以及准确率等评价指标方面都有比较好的结果。  相似文献   

8.
文章提出了一种基于复杂网络的节点影响力社区识别算法,并将其应用于藏文预训练语言模型的优化。社区划分发现了藏文语料库中词汇的隐含语义结构,进一步提升了预训练语言模型在下游任务中的表现[1]。  相似文献   

9.
传统复杂网络重要节点评估存在评估准确率较低的问题,因此对基于概念格的复杂网络重要节点评估进行研究。以重要节点评估网络示意图为基础,对网络直接与平均距离进行计算,从而复杂网络节点中心度,实现对复杂网络重要节点的评估。根据模拟实验证明,基于概念格的复杂网络重要节点评估与传统方法相比,评估准确率提高14%。  相似文献   

10.
张媛  李文娟  高鹏 《激光杂志》2022,43(2):139-143
为解决当前光通信节点调度中存在的调度效率低,网络覆盖率低的缺陷,提出基于数据挖掘的复杂光通信网络节点调度方法.结合当前光通信网络节点调度的研究进展,找出光通信网络节点调度方法的不足,结合光通信链路方式构建复杂光通信网络模型,利用学习 自动机计算方法实现复杂光通信网络节点调度.实验结果表明:当网络节点数量为500个时,设...  相似文献   

11.
在网络日益巨大化和复杂化的背景下,挖掘全局网络的社区结构代价较高。因此,基于给定节点的局部社区发现对研究复杂网络社区结构有重要的应用意义。现有算法往往存在着稳定性和准确性不高,预设定阈值难以获取等问题。该文提出一种基于边界节点识别的复杂网络局部社区发现算法,全面比较待合并节点的连接相似性进行节点聚类;并通过边界节点识别控制局部社区的规模和范围,从而获取给定节点所属社区的完整信息。在计算机生成网络和真实网络上的实验和分析证明,该算法能够自主挖掘给定节点所属的局部社区结构,有效地提升局部社区发现稳定性和准确率。  相似文献   

12.
社会网络中影响力传播的有效抑制是社会网络影响力传播机制研究所关注的问题之一。该文针对未知影响传播源,或传播源信息具有不确定性的情况,提出面向不确定性影响源的影响力传播抑制问题。首先,为有效提高抑制算法的执行效率,讨论竞争线性阈值传播模型下影响源传播能力的近似估计方法,进而提出有限影响源情况下,期望抑制效果最大化的抑制种子集挖掘算法。其次,对于大尺寸不确定性影响源的情况,考虑算法运行效率和抑制效果之间的有效折中,提出基于抽样平均近似的期望抑制效果最大化的抑制种子集挖掘算法。最后,在真实的社会网络数据集上,通过实验测试验证了所提出方法的有效性。  相似文献   

13.
This paper aims to effectively solve the problem of the influence maximization in social networks. For this purpose, an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed. Firstly, the network embedding-based community detection approach is developed, by which the social network is divided into several high-quality communities. Secondly, the solution of influence maximization is composed of the candidate stage and the greedy stage. The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm, and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm. Finally, experimental results demonstrate the superiority of the proposed method compared with existing methods, from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.  相似文献   

14.
15.
孙力娟  魏静  郭剑  肖甫  王汝传 《电子学报》2014,42(10):1907-1912
针对传感器网络中的节点冗余问题,提出了一种冗余判别方法来关闭冗余节点,以达到延长网络生命周期的目的.首先按邻居节点的不同位置对节点进行分类,研究了每组邻居节点的网络覆盖率与工作节点数k之间的约束关系,在此基础上,按不同的冗余法则对节点进行判断,关闭冗余节点.理论分析和实验结果表明,提出的算法能关闭网络中的冗余节点从而有效地延长网络的生存时间.  相似文献   

16.
17.
无线传感器网络的自身节点的定位对网络来说是非常重要的,传感器节点是随机分布在网络中的,这关系到网络最终的定位精度;节点自身定位的方法从节点的个数主要有单点定位和两个节点的定位,这里提出另一种定位方法,运用三个节点实现传感器网络的节点定位。  相似文献   

18.
In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically.
Yuguang FangEmail:

Yang Song   received his B.E. and M.E. degrees in Electrical Engineering from Dalian University of Technology, Dalian, China, and University of Hawaii at Manoa, Honolulu, U.S.A., in July 2004 and August 2006, respectively. Since September 2006, he has been working towards the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, Florida, USA. His research interests are wireless network, game theory, optimization and mechanism design. He is a student member of IEEE a member of Game Theory Society. Chi Zhang   received the B.E. and M.E. degrees in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in July 1999 and January 2002, respectively. Since September 2004, he has been working towards the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, Florida, USA. His research interests are network and distributed system security, wireless networking, and mobile computing, with emphasis on mobile ad hoc networks, wireless sensor networks, wireless mesh networks, and heterogeneous wired/wireless networks. Yuguang Fang   received a Ph.D. degree in Systems Engineering from Case Western Reserve University in January 1994 and a Ph.D degree in Electrical Engineering from Boston University in May 1997. He was an assistant professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology from July 1998 to May 2000. He then joined the Department of Electrical and Computer Engineering at University of Florida in May 2000 as an assistant professor, got an early promotion to an associate professor with tenure in August 2003 and to a full professor in August 2005. He holds a University of Florida Research Foundation (UFRF) Professorship from 2006 to 2009 and a Changjiang Scholar Chair Professorship with National Key Laboratory of Integrated Services Networks, Xidian University, China, from 2008 to 2011. He has published over 200 papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He is the recipient of the Best Paper Award in IEEE International Conference on Network Protocols (ICNP) in 2006 and the recipient of the IEEE TCGN Best Paper Award in the IEEE High-Speed Networks Symposium, IEEE Globecom in 2002. Dr. Fang is also active in professional activities. He is a Fellow of IEEE and a member of ACM. He has served on several editorial boards of technical journals including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing and ACM Wireless Networks. He has been actively participating in professional conference organizations such as serving as the Steering Committee Co-Chair for QShine, the Technical Program Vice-Chair for IEEE INFOCOM’2005, Technical Program Symposium Co-Chair for IEEE Globecom’2004, and a member of Technical Program Committee for IEEE INFOCOM (1998, 2000, 2003–2009).   相似文献   

19.
    
The wireless sensor networks composed of tiny sensor with the capability of monitoring the tangible changes for a wide range of applications are limited with the capabilities on processing and storage. Their limited capabilities make them seek the help of the cloud that provides the rented service of processing and storage. The dense deployment of the wireless sensor and their vulnerability to the unknown attacks, alterations make them incur difficulties in the process of the conveyance causing the modifications or the loss of the content. So, the paper proposes an optimized localization of the nodes along with the identification of the trusted nodes and minimum distance path to the cloud, allowing the target to have anytime and anywhere access of the content. The performance of the cloud infrastructure‐supported wireless sensor network is analyzed using the network simulator 2 on the terms of the forwarding latency, packet loss rate, route failure, storage, reliability, and the network longevity to ensure the capacities of the cloud infrastructure‐supported wireless sensor networks.  相似文献   

20.
传感器网络节点随机调度算法的覆盖度分析   总被引:1,自引:0,他引:1  
节点随机调度算法,是一种研究网络局部覆盖的重要方法,它无需知道节点位置信息、简单易用并有较高的实用价值。但是目前对该算法的研究成果均是基于布尔感知模型设计。布尔感知模型尽管简单,但不能真实地反映节点的感知能力。文中首次在概率感知模型下,分析了随机调度算法中网络覆盖度与节点数之间的关系,给出了节点数的下界值,并通过仿真验证了分析的正确性。  相似文献   

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