Terrorist Networks, Network Energy and Node Removal: A New Measure of Centrality Based on Laplacian Energy

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Social Networking

ISSN Print: 2169-3285
ISSN Online: 2169-3323
www.scirp.org/journal/sn
E-mail: sn@scirp.org

Google-based Impact Factor: 1
Citations

Journals Menu

"Terrorist Networks, Network Energy and Node Removal: A New Measure of Centrality Based on Laplacian Energy"
written by Xingqin Qi, Robert D. Duval, Kyle Christensen, Edgar Fuller, Arian Spahiu, Qin Wu, Yezhou Wu, Wenliang Tang, Cunquan Zhang,
published by Social Networking, Vol.2 No.1, 2013

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Large-scale group decision-making involving community representatives: A perspective of combining strong and weak ties
	Information Fusion, 2024

[2]	Introducing a New Edge Centrality Measure: The Connectivity Rank Index
	IEEE Transactions on Systems …, 2024

[3]	The Impact of Large-Scale Water Diversion Projects on the Water Supply Network: A Case Study in Southwest China
	Water, 2024

[4]	A Division of Defense Labor Across Nations
	2024

[5]	Influence Maximization and Online Social Capital for Entrepreneurs: Leveraging Social Networks to Achieve Business Goals.
	Social Capital in the Age …, 2023

[6]	Discovering important nodes of complex networks based on Laplacian spectra
	IEEE Transactions on …, 2023

[7]	Identifying spreading influence nodes for social networks
	Frontiers of Engineering Management, 2022

[8]	A novel method to identify influential nodes in complex networks based on gravity centrality
	Information Sciences, 2022

[9]	Identifying key nodes in interdependent networks based on Supra-Laplacian energy
	Journal of Computational Science, 2022

[10]	Screening Heuristics for the Evaluation of Covert Network Node Insertion Scenarios
	2022

[11]	Dismantling Interdependent Networks Based on Supra-Laplacian Energy
	… Conference on Science of Cyber Security, 2021

[12]	Diffusion profile embedding as a basis for graph vertex similarity
	Network Science, 2021

[13]	Unsupervised Common Particular Object Discovery and Localization by Analyzing a Match Graph
	2021

[14]	Graph active learning for GCN-based zero-shot classification
	2021

[15]	Analysing terrorist networks–An entropy‐driven method
	2021

[16]	Energy disruptive centrality with an application to criminal network
	2021

[17]	Effect of Infant Presence on Social Networks of Sterilized and Intact Wild Female Balinese Macaques (Macaca fascicularis)
	Animals, 2021

[18]	Brain hothubs and dark functional networks: correlation analysis between amplitude and connectivity for Broca's aphasia
	2020

[19]	电动汽车充电桩 CAN 总线协议的安全检测
	2020

[20]	基于时序和 TOPSIS 的社交网络节点重要性评价算法
	2020

[21]	Intervention algorithm for malicious information in online social networks based on trusted regulator
	2020

[22]	Two Laplacian energies and the relations between them
	2020

[23]	Measuring systemic risk and contagion in the European financial network
	2019

[24]	Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals
	2019

[25]	复杂网络关键节点组识别问题模型和算法研究
	2019

[26]	A new metric to quantify influence of nodes in social networks
	2019

[27]	Text Summarisation Using Laplacian Centrality-Based Minimum Vertex Cover
	2019

[28]	Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks
	2019

[29]	Research on Models and Algorithms for Critical Nodes Group Identification Problem in Complex Networks
	Journal of Frontiers of Computer Science & Technology, 2019

[30]	The Structural Vulnerability Analysis of Power Grids Based on Laplacian Centrality.
	J. Inf. Hiding Multim. Signal …, 2018

[31]	Consistency and differences between centrality measures across distinct classes of networks
	2018

[32]	Predicting Discussion Levels on Community Question-And-Answer Platforms using Expertise Networks
	2018

[33]	Profiling giants: the networks and influence of Buchanan and Tullock
	Public Choice, 2018

[34]	Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks
	2018

[35]	Evolving Networks and Social Network Analysis Methods and Techniques
	Social Media and Journalism-Trends, Connections, 2018

[36]	The Structural Vulnerability Analysis of Power Grids Based on Laplacian Centrality
	Journal of Information Hiding and Multimedia Signal Processing, 2018

[37]	Highly Repeatable Feature Point Detection in Images Using Laplacian Graph Centrality
	2018

[38]	Social Centrality using Network Hierarchy and Community Structure
	2018

[39]	Interest point detection based on Laplacian energy of local image network
	2017

[40]	On Biological Network Visualization: Understanding Challenges, Measuring the Status Quo, and Estimating Saliency of Visual Attributes
	2017

[41]	Identifizierung von führenden Köpfen in terroristischen Netzwerken–ein entropiebasiertes Verfahren–
	2017

[42]	Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks
	Complex Networks & Their Applications VI, 2017

[43]	基于多层次灰色关联分析的创新合作网络节点评估研究
	2017

[44]	Identifizierung von führenden Köpfen in terroristischen Netzwerken: ein entropiebasiertes Verfahren
	2017

[45]	Identifizierung von führenden Köpfen in terroristischen Netzwerken
	2017

[46]	基于 Kullback-Leibler 距离的网络节点一致性排序
	2016

[47]	The Important Node Assessment Method of Satellite Network Based on Near the Center
	2016

[48]	IPv6 AS 级 Internet 抗毁性研究
	2015

[49]	复杂网络的节点重要性综合评价
	2015

[50]	Analisis Dan Implementasi Probabilistic Partnership Index (ppi) Pada Laplacian Centrality Dalam Social Network Analysis
	2015

[51]	The node importance in actual complex networks based on a multi-attribute ranking method
	Knowledge-Based Systems, 2015

[52]	Analisis Metode Laplacian Centrality dalam Social Network Analysis menggunakan Probabilistic Affinity Index (PAI)
	2015

[53]	ANALISIS DAN IMPLEMENTASI PROBABILISTIC PARTNERSHIP INDEX (PPI) PADA LAPLACIAN CENTRALITY DALAM SOCIAL NETWORK ANALYSIS ANALYSIS AND IMPLEMENTATION OF PROBABILISTIC PARTNERSHIP INDEX (PPI) ON LAPLACIAN CENTRALITY IN SOCIAL NETWORK ANALYSIS
	network, 2015

[54]	A Risk Based Approach to Node Insertion Within Social Networks
	2015

[55]	Robust Features for Detecting Evasive Spammers in Twitter
	Advances in Artificial Intelligence, 2014

[56]	Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution
	NULL 2014

[57]	Social network Analysis Menggunakan Metode Laplacian Centrality Social network Analysis Using Laplacian Centrality Method
	MR Takkini, WM Adiwijaya - cdndata.ittelkom.ac.id, 2014

[58]	A Novel Method of Centrality in Terrorist Network
	Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on, 2014

[59]	Group disappearance in social networks with communities
	Social Network Analysis and Mining, 2013

[60]	How Do the Evolution and Innovation of Social Network Analysis Matter to Computer Science and Communications?
	Social Networking, 2013

[1]	The Impact of Large-Scale Water Diversion Projects on the Water Supply Network: A Case Study in Southwest China Water, 2024 DOI:10.3390/w16020357

[2]	Social Capital in the Age of Online Networking Advances in Social Networking and Online Communities, 2023 DOI:10.4018/978-1-6684-8953-6.ch010

[3]	Discovering Important Nodes of Complex Networks Based on Laplacian Spectra IEEE Transactions on Circuits and Systems I: Regular Papers, 2023 DOI:10.1109/TCSI.2023.3302332

[4]	Identifying key nodes in interdependent networks based on Supra-Laplacian energy Journal of Computational Science, 2022 DOI:10.1016/j.jocs.2022.101657

[5]	Identifying spreading influence nodes for social networks Frontiers of Engineering Management, 2022 DOI:10.1007/s42524-022-0190-8

[6]	Measuring systemic risk and contagion in the European financial network Empirical Economics, 2022 DOI:10.1007/s00181-021-02135-y

[7]	Analysing terrorist networks – An entropy‐driven method Expert Systems, 2022 DOI:10.1111/exsy.12720

[8]	Identifying spreading influence nodes for social networks Frontiers of Engineering Management, 2022 DOI:10.1007/s42524-022-0190-8

[9]	Identifying spreading influence nodes for social networks Frontiers of Engineering Management, 2022 DOI:10.1007/s42524-022-0190-8

[10]	A novel method to identify influential nodes in complex networks based on gravity centrality Information Sciences, 2022 DOI:10.1016/j.ins.2022.10.070

[11]	Identifying key nodes in interdependent networks based on Supra-Laplacian energy Journal of Computational Science, 2022 DOI:10.1016/j.jocs.2022.101657

[12]	Graph active learning for GCN-based zero-shot classification Neurocomputing, 2021 DOI:10.1016/j.neucom.2020.12.127

[13]	Energy disruptive centrality with an application to criminal network Communications in Nonlinear Science and Numerical Simulation, 2021 DOI:10.1016/j.cnsns.2021.105834

[14]	Graph active learning for GCN-based zero-shot classification Neurocomputing, 2021 DOI:10.1016/j.neucom.2020.12.127

[15]	Unsupervised Common Particular Object Discovery and Localization by Analyzing a Match Graph ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 DOI:10.1109/ICASSP39728.2021.9413549

[16]	Diffusion profile embedding as a basis for graph vertex similarity Network Science, 2021 DOI:10.1017/nws.2021.11

[17]	Effect of Infant Presence on Social Networks of Sterilized and Intact Wild Female Balinese Macaques (Macaca fascicularis) Animals, 2021 DOI:10.3390/ani11092538

[18]	Science of Cyber Security Lecture Notes in Computer Science, 2021 DOI:10.1007/978-3-030-89137-4_14

[19]	Energy disruptive centrality with an application to criminal network Communications in Nonlinear Science and Numerical Simulation, 2021 DOI:10.1016/j.cnsns.2021.105834

[20]	Brain hothubs and dark functional networks: correlation analysis between amplitude and connectivity for Broca’s aphasia PeerJ, 2020 DOI:10.7717/peerj.10057

[21]	Complex Networks and Their Applications VIII Studies in Computational Intelligence, 2020 DOI:10.1007/978-3-030-36687-2_17

[22]	Text Summarisation Using Laplacian Centrality-Based Minimum Vertex Cover Journal of Information & Knowledge Management, 2019 DOI:10.1142/S0219649219500503

[23]	Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) Lecture Notes in Computational Vision and Biomechanics, 2019 DOI:10.1007/978-3-030-00665-5_68

[24]	Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals IEEE Transactions on Computational Social Systems, 2019 DOI:10.1109/TCSS.2019.2902456

[25]	A new metric to quantify influence of nodes in social networks International Journal of Modern Physics B, 2019 DOI:10.1142/S0217979219501868

[26]	Consistency and differences between centrality measures across distinct classes of networks PLOS ONE, 2019 DOI:10.1371/journal.pone.0220061

[27]	Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) Lecture Notes in Computational Vision and Biomechanics, 2019 DOI:10.1007/978-3-030-00665-5_68

[28]	Social Media and Journalism - Trends, Connections, Implications 2018 DOI:10.5772/intechopen.79041

[29]	Complex Networks & Their Applications VI Studies in Computational Intelligence, 2018 DOI:10.1007/978-3-319-72150-7_28

[30]	Profiling giants: the networks and influence of Buchanan and Tullock Public Choice, 2018 DOI:10.1007/s11127-018-0535-3

[31]	Social centrality using network hierarchy and community structure Data Mining and Knowledge Discovery, 2018 DOI:10.1007/s10618-018-0582-x

[32]	Interest point detection based on Laplacian energy of local image network 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2017 DOI:10.1109/WiSPNET.2017.8299719

[33]	The node importance in actual complex networks based on a multi-attribute ranking method Knowledge-Based Systems, 2015 DOI:10.1016/j.knosys.2015.03.026

[34]	Understanding Large Temporal Networks and Spatial Networks 2014 DOI:10.1002/9781118915370.refs

[35]	A Novel Method of Centrality in Terrorist Network 2014 Seventh International Symposium on Computational Intelligence and Design, 2014 DOI:10.1109/ISCID.2014.156

[36]	Advances in Artificial Intelligence Lecture Notes in Computer Science, 2014 DOI:10.1007/978-3-319-06483-3_28

[37]	Group disappearance in social networks with communities Social Network Analysis and Mining, 2013 DOI:10.1007/s13278-013-0112-1

[38]	How Do the Evolution and Innovation of Social Network Analysis Matter to Computer Science and Communications? Social Networking, 2013 DOI:10.4236/sn.2013.23013

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Social Networking

Social Networking

Journals Menu

Home

About SCIRP

Service

Policies