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CENTRALITY ESTIMATION IN LARGE NETWORKS

ULRIK BRANDES, CHRISTIAN PICH
2007 International Journal of Bifurcation and Chaos in Applied Sciences and Engineering  
Centrality indices are an essential concept in network analysis.  ...  Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations.  ...  We thank Simon Endele for his help in running the experiments and Eric Kolaczyk for interesting comments.  ... 
doi:10.1142/s0218127407018403 fatcat:jhietih2efgvji7y5gfs7k725i

Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks [article]

Mostafa Haghir Chehreghani and Talel Abdessalem and and Albert Bifet
2017 arXiv   pre-print
Betweenness centrality is an important index widely used in different domains such as social networks, traffic networks and the world wide web.  ...  The stationary distribution of our MCMC sampler is the optimal sampling proposed for betweenness centrality estimation.  ...  [20] proposed a fully dynamic algorithm for estimating betweenness centrality of all vertices in a large dynamic network.  ... 
arXiv:1704.07351v3 fatcat:hydhecvhdnhttkp3bmofalewxq

An Estimated Closeness Centrality Ranking Algorithm and Its Performance Analysis in Large-Scale Workflow-supported Social Networks

2016 KSII Transactions on Internet and Information Systems  
This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks.  ...  To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking  ...  Conclusion In this paper, we have proposed an estimated closeness centrality algorithm to be applied to large-scale workflow-supported social networks.  ... 
doi:10.3837/tiis.2016.03.031 fatcat:iilwwu42crbnnpktzkticfy6ia

Performance Analysis of an Estimated Closeness Centrality Ranking Algorithm in Large-Scale Workflow-supported Social Networks
대규모 워크플로우 소셜 네트워크의 추정 근접 중심도 랭킹 알고리즘 성능 분석

Jawon Kim, Hyun Ahn, Kwanghoon Kim
2015 Journal of Internet Computing and services  
This paper implements an estimated closeness centrality ranking algorithm in large-scale workflow-supported social networks and performance analyzes of the algorithm.  ...  Existing algorithm has a time complexity problem which is increasing performance time by network size. This problem also causes ranking process in large-scale workflow-supported social networks.  ...  참고문헌(Reference) [1] Jawon Kim, Hyun Ahn, Hyunah Kim, Minjae Park, Kwanghoon Kim, "Performance Analysis of an Estimated Closeness Centrality based Ranking Algorithm in Large Scale Workflow-supported Social  ... 
doi:10.7472/jksii.2015.16.3.71 fatcat:qpifax735jdtrmtmv3uo5t2fgu

Resampling-Based Gap Analysis for Detecting Nodes with High Centrality on Large Social Network [chapter]

Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda
2015 Lecture Notes in Computer Science  
We address a problem of identifying nodes having a high centrality value in a large social network based on its approximation derived only from nodes sampled from the network.  ...  values if it can divide the ordered list of nodes into two groups so that any node in one group has a higher centrality value than any one in another group with a given confidence level.  ...  Since a social network on the web can easily grow in size, it is crucial to efficiently compute values of such a centrality to analyze a large network.  ... 
doi:10.1007/978-3-319-18038-0_11 fatcat:tori5t3ngvfudf64jwasqioshm

Global Rank Estimation [article]

Akrati Saxena, S. R. S. Iyengar
2017 arXiv   pre-print
In this work, we also discuss how to apply these methods for degree and closeness centrality rank estimation.  ...  So, it is not feasible for real-life applications due to the large size and dynamic nature of real world networks.  ...  Approximation Methods: The computation cost of global centrality measures is very high in large scale networks.  ... 
arXiv:1710.11341v1 fatcat:2awuzwu7cndx3gufygxgt3la2y

Dynamic Social Network Analysis with Heterogeneous Sensors in Ambient Environment

Sho Tsugawa, Hiroyuki Ohsaki, Yuichi Itoh, Naoaki Ono, Keiichiro Kagawa, Kazuki Takashima
2014 Social Networking  
This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient  ...  We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications.  ...  In Section 2, we discuss challenges in realizing large-scale dynamic social network analysis in real environments.  ... 
doi:10.4236/sn.2014.31002 fatcat:ja6vkepe5jdz7pgpz5jpfo4nke

Centrality Measures: A Tool to Identify Key Actors in Social Networks [article]

Rishi Ranjan Singh
2020 arXiv   pre-print
Experts from several disciplines have been widely using centrality measures for analyzing large as well as complex networks.  ...  These measures rank nodes/edges in networks by quantifying a notion of the importance of nodes/edges. Ranking aids in identifying important and crucial actors in networks.  ...  in a large network based on some centrality measure.  ... 
arXiv:2011.01627v1 fatcat:hsocyivf5jgstkled67osd6w6q

Neural network-based reputation model in a distributed system

Weihua Song, V.V. Phoha
2004 Proceedings. IEEE International Conference on e-Commerce Technology, 2004. CEC 2004.  
Current centralized trust models are inappropriate to apply in a large distributed multi-agent system, due to various evaluation models and partial observations in local level reputation management.  ...  The global reputation model is a novel application of neural network techniques in distributed reputation evaluations.  ...  Conclusions Centralized trust models are not appropriate to apply in a large and sparse distributed trust system.  ... 
doi:10.1109/icect.2004.1319751 dblp:conf/wecwis/SongP04 fatcat:p47toketcnblbj2yiqzkyx3sky

Efficient and Accurate Robustness Estimation for Large Complex Networks [article]

Sebastian Wandelt, Xiaoqian Sun
2016 arXiv   pre-print
We propose a new algorithm for estimating the robustness of a network in sub-quadratic time, i.e., significantly faster than betweenness centrality.  ...  Our work contributes towards scalable, yet accurate methods for robustness estimation of large complex networks.  ...  The results of our study show that efficient, yet accurate robustness estimation is possible even for very large networks.  ... 
arXiv:1608.03988v1 fatcat:nvqyechwc5gxrkdnsnpyzcas5q

Estimating Psychological Networks and their Accuracy: A Tutorial Paper [article]

Sacha Epskamp, Denny Borsboom, Eiko I. Fried
2017 arXiv   pre-print
Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods.  ...  and centrality estimates for different variables differ from each other.  ...  We simulated a dataset of 500 individuals (typically regarded a moderately large sample size in psychology) using the network in Figure 1 and estimated a network structure based on the simulated data  ... 
arXiv:1604.08462v4 fatcat:e44cetql7vgonbjpfhpofxpgse

Estimating psychological networks and their accuracy: A tutorial paper

Sacha Epskamp, Denny Borsboom, Eiko I. Fried
2017 Behavior Research Methods  
Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods.  ...  and centrality estimates for different variables differ from each other.  ...  distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in  ... 
doi:10.3758/s13428-017-0862-1 pmid:28342071 pmcid:PMC5809547 fatcat:2q6vyb2fsjep3hihkdmp2ldnue

Estimating point centrality using different network sampling techniques

Joseph Galaskiewicz
1991 Social Networks  
estimate the true indegree and point centrality of actors in our networks using network sampling techniques.  ...  Because of the very large and very small number of lines going to popular and unpopular actors respectively, there may be problems that analysts face in estimating point centrality.  ... 
doi:10.1016/0378-8733(91)90002-b fatcat:ly6i3jhzczep3pimhsj5o6bkcm

K-path centrality

Tharaka Alahakoon, Rahul Tripathi, Nicolas Kourtellis, Ramanuja Simha, Adriana Iamnitchi
2011 Proceedings of the 4th Workshop on Social Network Systems - SNS '11  
especially in very large networks probability 1 − 2 , C B [v ] can be estimated within (1 ± 1/ ) · C B [v ] with t K-path Centrality Definition (K-Path Centrality) For every vertex v of a graph G  ...  Measure in Social Networks Centrality Metrics in Social Network Analysis K-path: A New Centrality Metric Experiments Summary Motivation Intuition Our Contributions Basic Assumptions and Definition  ...  Summary We introduced an alternative centrality metric for betweeness centrality, κ-path centrality, that: identifies with high accuracy the top betweenness centrality nodes in a graph; -correlation between  ... 
doi:10.1145/1989656.1989657 dblp:conf/sns/AlahakoonTKSI11 fatcat:bdvwm4ayujfznlcnep7kc3ugsa

Distributed Hybrid Two-Stage Multi-Sensor Fusion for Cooperative Modulation Classification in Large-Scale Wireless Sensor Networks

Markovic, Sokolovic, Dukic
2019 Sensors  
The adopted distributed concept represents a flexible and scalable solution that is suitable for implementation of large-scale networks.  ...  the partial information loss while performing centralized fusion).  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/s19194339 fatcat:n6t2syh5hjcajlaxvbr73lis6m
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