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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. ...
Betweenness centrality is an important centrality measure widely used in social network analysis, route planning etc. ...
For unweighted graphs, it gives an O(T m) time algorithm for approximate betweenness centrality computation. ...
doi:10.1093/comjnl/bxu003
fatcat:dg5gjjv4drevxhuurdmslr2r3i
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. ...
Betweenness centrality is an important centrality measure widely used in social network analysis, route planning etc. ...
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
Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
[article]
2021
arXiv
pre-print
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We show that this algorithm provides an (ϵ,δ)-approximation to the betweenness score of r. ...
Betweenness centrality is an important index widely used to analyze networks. ...
Abdessalem / Computing Betweenness Centrality in Directed Graphs We compare our method against the most efficient existing algorithm for approximating betweenness centrality, which is KADABRA [31] . ...
arXiv:1708.08739v2
fatcat:tnrh4vcgsffgnk77istroiv2du
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. ...
Acknowledgment The authors would like to thank the IIT Ropar HPC committee for providing the resources for performing experiments. They also would like to thank S.R.S. ...
arXiv:1409.6470v3
fatcat:3cc6hzo74zgu3g7wcjtpvqjd6u
Approximation Algorithm for Shortest Path in Large Social Networks
2020
Algorithms
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We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. ...
Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall's algorithms are limited to small networks due to computational complexity and cost. ...
Algorithms 2020, 13, 36 ...
doi:10.3390/a13020036
fatcat:lqroqgmbq5a2nlpk67zrafnh4m
An approximation algorithm for shortest path based on the hierarchy networks
[article]
2015
arXiv
pre-print
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2014 pre-print arXiv:1405.4051v1 fatcat:3hesaee3s5ghbcrvnqaevg7c7e
It is a critical issue to compute the shortest paths between nodes in networks. ...
Exact algorithms for shortest paths are usually inapplicable for large scale networks due to the high computational complexity. ...
The algorithm approximates the distances by means of the shortest paths between the central nodes computed by Dijkstra's algorithm. ...
arXiv:1405.4051v2
fatcat:2er3veanajf4rffnn4k4wylaia
Biharmonic Distance Related Centrality for Edges in Weighted Networks
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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We give an efficient algorithm that provides an approximation of biharmonic distance for all edges in nearly linear time of the number of edges, with a high probability. ...
For an arbitrary edge, we explicitly determine the change of the Kirchhoff index and express it in terms of the biharmonic distance between its end nodes, and thus call this centrality as biharmonic distance ...
Moreover, we provided an approximation algorithm with probabilistic guarantee, which computes BDRC for all edges in a network in nearly-linear time. ...
doi:10.24963/ijcai.2018/503
dblp:conf/ijcai/YiS0Z18
fatcat:hfrj3vpozvbqdkv7wo72vqmkwq
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. ...
Tree Estimation We introduce an algorithm for computing original centralities efficiently. ...
doi:10.1007/978-3-642-20847-8_4
fatcat:fayqfov5yvdk5izj4spybm3wla
An Efficient Approximation of Betweenness Centrality for Uncertain Graphs
2019
IEEE Access
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To address this challenging issue, in this paper, we propose the concept of possible shortest paths and develop a metric to approximate the betweenness centrality for uncertain graphs. ...
The experimental results show that our approach can approximate the centrality of uncertain graphs accurately with high efficiency. ...
Then we develop an efficient algorithm to approximate the betweenness centrality of a probabilistic graph. ...
doi:10.1109/access.2019.2915974
fatcat:eufp2d56hfcn3mgqptzjrce57y
Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities
[article]
2018
arXiv
pre-print
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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. ...
An important index widely used to analyze social and information networks is betweenness centrality. ...
RELATED WORK Brandes [7] introduced an efficient algorithm for computing betweenness centrality of all vertices, which is performed respectively in O(|V (G)||E(G)|) and O(|V (G)||E(G)| + |V (G)| 2 log ...
arXiv:1810.10094v1
fatcat:ql4p4pzinzhlxe4g76kane6gsq
Efficient algorithms for updating betweenness centrality in fully dynamic graphs
2016
Information Sciences
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Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph. ...
Betweenness centrality of a vertex (edge) in a graph is a measure for the relative participation of the vertex (edge) in the shortest paths in the graph. ...
If we use an approximation betweenness centrality computation algorithm to calculate local betweenness centrality, our algorithm becomes an approximate update algo-590 rithm. ...
doi:10.1016/j.ins.2015.07.053
fatcat:je75jmmpwzaadji4y3za4s626i
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. ...
We present a novel approach based on graph coarsening for approximating values of betweenness centrality, when new edges are inserted. ...
To that end an efficient random-sampling-based algorithm is used to estimate the betweenness centrality of the top-K vertices in the graph with high probability. ...
doi:10.1016/j.procs.2015.11.011
fatcat:mwhtka25fbfjpj2nkhgtjneqxq
Brief Announcement: Massively Parallel Approximate Distance Sketches
2019
International Symposium on Distributed Computing
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This result has additional applications such as the first polylogarithmic time algorithm for constant approximate single-source shortest paths for weighted graphs in the low memory MPC setting. ...
Data structures that allow efficient distance estimation have been extensively studied both in centralized models and classical distributed models. ...
As a side effect of our techniques, we immediately get an algorithm for computing approximate single-source shortest paths (SSSP). ...
doi:10.4230/lipics.disc.2019.42
dblp:conf/wdag/DinitzN19
fatcat:3xjwtruhrvfbllwl5lwflt4pcu
Centrality Measures: A Tool to Identify Key Actors in Social Networks
[article]
2020
arXiv
pre-print
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Experts from several disciplines have been widely using centrality measures for analyzing large as well as complex networks. ...
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. ...
[212] gave an efficient algorithm to compute group closeness centrality for disk-resident networks. Chen et al. ...
arXiv:2011.01627v1
fatcat:hsocyivf5jgstkled67osd6w6q
Efficient extraction of high centrality vertices in distributed graphs
2014
2014 IEEE High Performance Extreme Computing Conference (HPEC)
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Betweenness centrality (BC) is an important measure for identifying high value or critical vertices in graphs, in variety of domains such as communication networks, road networks, and social graphs. ...
We evaluate the proposed algorithms using a mix of real-world and synthetic graphs on an HPC cluster and analyze its strengths and weaknesses. ...
The main motivation is that good approximations can be an acceptable alternative to exact scores. In Bader et al. [14] an efficient approximate algorithm based on an adaptive sampling is presented. ...
doi:10.1109/hpec.2014.7040974
dblp:conf/hpec/KumbhareFRP14
fatcat:pq3uwfld7nbspcabvumvjbgrs4
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