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Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
[article]
2021
arXiv
pre-print
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In this paper, first given a directed network G and a vertex r ∈ V(G), we propose an exact algorithm to compute betweenness score of r. ...
Graphs (networks) are an important tool to model data in different domains. Real-world graphs are usually directed, where the edges have a direction and they are not symmetric. ...
Acknowledgement This work has been supported in part by the ANR project IDOLE. ...
arXiv:1708.08739v2
fatcat:tnrh4vcgsffgnk77istroiv2du
Efficient Centrality Monitoring for Time-Evolving Graphs
[chapter]
2011
Lecture Notes in Computer Science
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Sniper is based on two ideas: (1) It computes approximate centrality by reducing the original graph size while guaranteeing the answer exactness, and (2) It terminates unnecessary distance computations ...
Previous approaches to this problem find the lowest centrality nodes efficiently at the expense of exactness. ...
For directed graphs, we apply Definition 1 for each direction to handle directed edges of approximate graphs. ...
doi:10.1007/978-3-642-20847-8_4
fatcat:fayqfov5yvdk5izj4spybm3wla
An Efficient Algorithm for Approximate Betweenness Centrality Computation
2014
Computer journal
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In this paper, we propose a generic randomized framework for unbiased approximation of betweenness centrality. ...
However, even for mid-size networks, it is practically intractable to compute exact betweenness scores. ...
For unweighted graphs, it gives an O(T m) time algorithm for approximate betweenness centrality computation. ...
doi:10.1093/comjnl/bxu003
fatcat:dg5gjjv4drevxhuurdmslr2r3i
Fast Exact and Approximate Computation of Betweenness Centrality in Social Networks
[chapter]
2014
Lecture Notes in Social Networks
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Social networks are naturally represented as graphs, consequently graph theory and efficient graph algorithms play an important role in social network analysis. ...
This means that, in general, for fairly large networks the computation of this index based on a direct application of its definition becomes impractical, having complexity O(n 3 ), for a graph with n nodes ...
We also give a fast sampling-based algorithm that computes an approximation of the betweenness centrality values of the residual network while returns the exact value for the tree-nodes. ...
doi:10.1007/978-3-319-05912-9_3
fatcat:kow6lt22lzhpdl53mf7uxcc2ne
Centrality Measures on Big Graphs
2016
Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
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In our presentation, we begin from exact algorithms and then progress to approximation algorithms, including sampling-based ones, and to highly-scalable MapReduce algorithms for huge graphs, both for exact ...
Our goal is to show how advanced algorithmic techniques and scalable systems can be used to obtain efficient algorithms for an important graph mining task, and to encourage research in the area by highlighting ...
(d) Exact algorithms for betweenness centrality in a dynamic graph [12, 15, 17] . (e) Exact algorithms for closeness centrality in a dynamic graph [21] . ...
doi:10.1145/2872518.2891063
dblp:conf/www/BonchiMR16
fatcat:crfgpu5u75f7fcowrwowmnw3a4
An efficient algorithm for approximate betweenness centrality computation
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
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In this paper, we propose a generic randomized framework for unbiased approximation of betweenness centrality. ...
However, even for mid-size networks, it is practically intractable to compute exact betweenness scores. ...
For unweighted graphs, it gives an O(T m) time algorithm for approximate betweenness centrality computation. ...
doi:10.1145/2505515.2507826
dblp:conf/cikm/Chehreghani13
fatcat:zpyrfolqtneajowe6teevoo6li
Heuristic Algorithm for Approximation Betweenness Centrality Using Graph Coarsening
2015
Procedia Computer Science
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Here we focus on approximating the computation of betweenness centrality for dynamically changing graphs. ...
Our approach demonstrates more than 60% speedup compared to the exact recalculation of the betweenness centrality for dynamically changing graphs. ...
Next, we compute the exact values of betweenness centrality metric for these two "small" graphs in lines 3 and 4. ...
doi:10.1016/j.procs.2015.11.011
fatcat:mwhtka25fbfjpj2nkhgtjneqxq
An Efficient Heuristic for Betweenness-Ordering
[article]
2017
arXiv
pre-print
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In this paper, we propose an efficient heuristic to determine the betweenness-ordering of k vertices (where k is very less than the total number of vertices) without computing their exact betweenness indices ...
Betweenness centrality comes as a handy tool to analyze such systems, but betweenness computation is a daunting task in large size networks. ...
Iyengar and the Malgudi team at IIT Ropar for their comments to improve the presentation of the paper. ...
arXiv:1409.6470v3
fatcat:3cc6hzo74zgu3g7wcjtpvqjd6u
Fast Exact Computation of betweenness Centrality in Social Networks
2012
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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The computation of the betweenness centrality index is a well established method for network data analysis and it is also important as subroutine in more advanced algorithms, such as the Girvan-Newman ...
In this paper we present a new approach for the computation of the betweenness centrality, which speeds up considerably Brandes' algorithm (the current state of the art) in the context of social networks ...
In 2001 Brandes [9] developed the asymptotically fastest exact algorithm to date, that exploits a recursive formula for computing partial betweenness indices efficiently. ...
doi:10.1109/asonam.2012.79
dblp:conf/asunam/BaglioniGPL12
fatcat:kodftveqkbg7tarrpwlnlfqkta
Betweenness Centrality Approximations for an Internet Deployed P2P Reputation System
2011
2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum
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We show that these approximations are efficient and highly accurate in scale-free and Bartercast graphs, but less so in random graphs. ...
BC is a powerful metric for identifying central nodes in complex network analysis, but its computation in large and dynamic networks is costly, and previously proposed approximation methods are only designed ...
number of common nodes in the sequences of the top-l (l = 10, 25 and 50) most central nodes of the exact computation of BC and the approximations k-BC, P -BC and S-BC. number of transpositions between ...
doi:10.1109/ipdps.2011.317
dblp:conf/ipps/GkorouPE11
fatcat:2yw6zhfalfha3ab6ksuwdiol4y
Better Approximation of Betweenness Centrality
[chapter]
2008
2008 Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments (ALENEX)
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Betweenness has been used in diverse applications, e.g., social network analysis or route planning. Since exact computation is prohibitive for large networks, approximation algorithms are important. ...
Our best new schemes yield significantly better approximation than before for many real world inputs. In particular, we also get good approximations for the betweenness of unimportant nodes. 90 ...
We would like to thank Reinhard Bauer, Ulrik Brandes, Daniel Delling and Dorothea Wagner for interesting discussions. Several anonymous reviewers provided valuable suggestions. ...
doi:10.1137/1.9781611972887.9
dblp:conf/alenex/GeisbergerSS08
fatcat:otsw5qw4sze6dczip5gewgp6pm
Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities
[article]
2018
arXiv
pre-print
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In this paper, first given a directed network G and a vertex r∈ V(G), we present a novel adaptive algorithm for estimating betweenness score of r. ...
We show that compared to the well-known existing methods, our algorithm gives a more efficient (λ,δ)-approximation. Then, we propose a novel algorithm for estimating k-path centrality of r. ...
Finally, in [12] the authors presented exact and approximate algorithms for computing betweenness centrality of one vertex or a small set of vertices in directed graphs. ...
arXiv:1810.10094v1
fatcat:ql4p4pzinzhlxe4g76kane6gsq
Estimating High Betweenness Centrality Nodes via Random walk in Social Networks
2020
Journal of Information Processing
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Several algorithms have been studied to efficiently compute the top-k nodes with the highest betweenness centrality on a graph where all the data is available. ...
Then, we obtain unbiased estimates of the ego betweenness centrality of sampled nodes and approximate the top-k nodes with the highest betweenness centrality as the top-k nodes with the highest estimated ...
Acknowledgments This work was supported by New Energy and Industrial Technology Development Organization (NEDO). ...
doi:10.2197/ipsjjip.28.436
fatcat:ifsumo5zvrb3hljfzxcnofirry
Centrality Measures: A Tool to Identify Key Actors in Social Networks
[article]
2020
arXiv
pre-print
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In this chapter, we summarize some of the centrality measures that are extensively applied for mining social network data. We also discuss various directions of research related to these measures. ...
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. ...
Approximation Algorithms for Dynamic Graphs Several literature on centrality measures studied either dynamic algorithms or approximation algorithms for computation of centrality scores in the last two ...
arXiv:2011.01627v1
fatcat:hsocyivf5jgstkled67osd6w6q
Summarizing Documents by Measuring the Importance of a Subset of Vertices within a Graph
2009
2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology
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Given a cluster of documents, we firstly construct a graph where each vertex represents a sentence and edges are created according to the asymmetric relationship between sentences. ...
The importance of such a super-vertex is quantified as super-centrality, a quantitative measure for the importance of a subset of vertices within the whole graph. ...
Xiaoyan Zhu for her kind support. ...
doi:10.1109/wi-iat.2009.46
pmid:21243102
pmcid:PMC3019581
dblp:conf/webi/ChenHL09
fatcat:c7xvfxtwpbdkjiuoo7ocusidnm
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