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Measures of centrality based on the spectrum of the Laplacian

Scott D. Pauls, Daniel Remondini
2012 Physical Review E  
We introduce a family of new centralities, the k-spectral centralities. k-Spectral centrality is a measurement of importance with respect to the deformation of the graph Laplacian associated with the graph  ...  Due to this connection, k-spectral centralities have various interpretations in terms of spectrally determined information. We explore this centrality in the context of several examples.  ...  The computation of 1-spectral centrality for a network of N of nodes is on the order of O(N 3 ).  ... 
doi:10.1103/physreve.85.066127 pmid:23005182 fatcat:eqtcttvzzrehldhzirwllrbldq

Dynamical Influence of Nodes Revisited: A Markov Chain Analysis of Epidemic Process on Networks

Ping Li, Jie Zhang, Xiao-Ke Xu, Michael Small
2012 Chinese Physics Letters  
analysis of epidemic dynamics on networks.  ...  (Received 11 January 2012) We provide a theoretical analysis of node importance from the perspective of dynamical processes on networks.  ... 
doi:10.1088/0256-307x/29/4/048903 fatcat:ydhaeiarffg65dod72dfcl7bzm

Resilience in urban networked infrastructure: the case of Water Distribution Systems [article]

Antonio Candelieri, Ilaria Giordani, Andrea Ponti, Francesco Archetti
2020 arXiv   pre-print
This paper has two objectives: first to show how a set of global measures can be obtained using techniques from network theory, in particular how the spectral analysis of the adjacency and Laplacian matrices  ...  Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on net-wide measures of connectivity  ...  Regionale) 2014-2020, innovation call "Accordi per la Ricerca e l'Innova- We greatly acknowledge the DEMS Data Science Lab for supporting this work by providing computational resources (DEMS -Department of  ... 
arXiv:2006.14622v1 fatcat:vhpx4nvourfipolmkpa5sb6re4

Utility of network integrity methods in therapeutic target identification

Qian Peng, Nicholas J. Schork
2014 Frontiers in Genetics  
We consider centrality measures based on both graph theory and spectral graph theory.  ...  Such analysis requires exploring network properties, in particular the importance of individual network nodes (i.e., genes).  ...  Thus, in addition to common measures of network centrality which focus on cancer-related genes, we also investigate the utility of centralities based on spectral graph theory, including spectral gap centrality  ... 
doi:10.3389/fgene.2014.00012 pmid:24550933 pmcid:PMC3909879 fatcat:beqwxgvddbcghkpkvpzhkofyaq

Applications of Graph Spectral Techniques to Water Distribution Network Management

Armando di Nardo, Carlo Giudicianni, Roberto Greco, Manuel Herrera, Giovanni Santonastaso
2018 Water  
However, there are a series of advantages of focusing the analysis only on the network topology.  ...  Firstly, it is done a robustness analysis by computing the strength of the network connectivity using a number of spectral metrics.  ...  Author Contributions: Each of the authors contributed to the design, analysis and writing of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Water 2018, 10, 45  ... 
doi:10.3390/w10010045 fatcat:6rlrwor5ufbt7kobgvobtx7nhe

Structural Patterns in Complex Networks through Spectral Analysis [chapter]

Ernesto Estrada
2010 Lecture Notes in Computer Science  
First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map.  ...  Estrada, Ernesto (2010) Structural patterns in complex networks through spectral analysis. Abstr act.  ...  This work is partially funded by the Principal of the University of Strathclyde through the New Professors' Fund.  ... 
doi:10.1007/978-3-642-14980-1_4 fatcat:xufd6tsxibfwrl4cknxmgfwt3u

Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain

Gabriele Lohmann, Daniel S. Margulies, Annette Horstmann, Burkhard Pleger, Joeran Lepsien, Dirk Goldhahn, Haiko Schloegl, Michael Stumvoll, Arno Villringer, Robert Turner, Olaf Sporns
2010 PLoS ONE  
Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series.  ...  In this work, we introduce an alternative assumption-and parameter-free method based on a particular form of node centrality called eigenvector centrality.  ...  Node centrality is a key concept in social network analysis of which several competing definitions exist and some of which have been applied to fMRI data analysis in the past [5, 9] .  ... 
doi:10.1371/journal.pone.0010232 pmid:20436911 pmcid:PMC2860504 fatcat:ol6mcswi4nhdxbqmx5kaehzbia

Structural Analysis in Transit System Using Network Theory Case of Guadalajara, Mexico

Orlando Barraza, Miquel Estrada
2021 Urban Science  
to their nodes or links.  ...  Structural analysis in a transit network is a key aspect used to evaluate in a planning process.  ...  On the other end, the scenario of Betweenness centrality remotion ends with 884,540 nodes and 478,172 for the scenario of Closeness centrality.  ... 
doi:10.3390/urbansci5040087 fatcat:qjruxps4djg5bonvss6qcp6mda

Syntax is from Mars while Semantics from Venus! Insights from Spectral Analysis of Distributional Similarity Networks [article]

Chris Biemann, Monojit Choudhury, Animesh Mukherjee
2009 arXiv   pre-print
We study the global topology of the syntactic and semantic distributional similarity networks for English through the technique of spectral analysis.  ...  In recent times, there have been some work on spectral analysis of linguistic networks as well.  ...  Note that the degree of a node can Spectrum of DSNs Spectral analysis refers to the systematic study of the eigenvalues and eigenvectors of a network.  ... 
arXiv:0906.1467v1 fatcat:ufrm3j6rmng4tfg3imyqyr4bfa

A novel framework in complex network analysis: Considering both structure of relations and individual characteristics in closeness centrality computation

F Barzinpour, B. H. Ali Ahmadi
2013 International Journal of Industrial Engineering Computations  
This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics.  ...  Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures.  ...  The method combines spectral techniques, considering individual node attributes, and cluster analysis as preprocessing stages.  ... 
doi:10.5267/j.ijiec.2013.02.001 fatcat:r4dur4bbffaptafq2ddx3spe6y

Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection [article]

Pin-Yu Chen, Sutanay Choudhury, Alfred O. Hero
2016 arXiv   pre-print
dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices.  ...  spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes  ...  Degree is defined as the number of edges associated with a we focus on hop walk statistics. An h-hop walk of a node on a graph node.  ... 
arXiv:1512.07372v2 fatcat:pnaa2oq2vndynm6ztmuc3xdrzq

Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks

Craigory Coppola, Heba Elgazzar
2020 International Journal of Artificial Intelligence & Applications  
The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality.  ...  Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes.  ...  Spectral Clustering One clustering technique is called spectral clustering. Spectral clustering operates on the similarity graph between nodes [20] .  ... 
doi:10.5121/ijaia.2020.11106 fatcat:hgmsowogf5ardfpcrofr34urhu

On Symptom Distribution Regularity of Insomnia based on Node2vec and Spectral Clustering (Preprint)

Fang Hu, Liuhuan Li, Xiaoyu Huang, Xingyu Yan, Panpan Huang
2019 JMIR Medical Informatics  
using evaluation metrics of node centrality.  ...  We used four evaluation metrics of node centrality to discover the core symptom nodes from multiple aspects.  ...  Acknowledgments This study was funded by the National Natural Science Foundation of China (81874414), and the Natural Science Foundation of Hubei Province (2018CFB259).  ... 
doi:10.2196/16749 pmid:32297869 fatcat:su7orlqunffjdho4vdagade45e

Cortical network modulation during paced arm movements

S. F. Storti, E. Formaggio, P. Manganotti, G. Menegaz
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
While ERD provides an estimate of the differences in power spectral densities between task and rest conditions, coherence allows assessing the level of synchronization between the recorded signals and  ...  graph analysis enables the estimation of the functional network topology.  ...  The brain network was constructed based on the unthresholded spectral coherence values of the 19 electrodes/nodes, using the corresponding Coh ω (x, y) as the weight of the edge connecting x and y nodes  ... 
doi:10.1109/eusipco.2015.7362854 dblp:conf/eusipco/StortiFMM15 fatcat:q6qxma5oivgm5jp7nafp5modlm

Using coherencies to examine network evolution and co-evolution

George A. Barnett, Ke Jiang, Jesse R. Hammond
2015 Social Network Analysis and Mining  
An earlier draft of this manuscript was presented to the Preconference on Social and Semantic Networks in Communication Research of the International Communication Association, Seattle, WA, May, 2014.  ...  This is typically accomplished after decomposing the physical or social process using spectral analysis or frequency domain analysis on the measures of each process.  ...  Spectral analysis or frequency domain analysis or spectral density estimation is the procedures that allow the decomposition of a complex over-time process into simpler parts.  ... 
doi:10.1007/s13278-015-0297-6 fatcat:rx6z6swuwnb3dciscxnize4jlm
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