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Computing top-k Closeness Centrality Faster in Unweighted Graphs
[article]

2017
*
arXiv
*
pre-print

For example, we are able to

arXiv:1704.01077v2
fatcat:gc32wvmmdzhczka6rv7h3c7ajy
*compute*the*top**k*nodes*in*few dozens of seconds*in*real-world networks with millions of nodes and edges. ... Given a connected*graph*G=(V,E), the*closeness**centrality*of a vertex v is defined as n-1/∑_w ∈ V d(v,w). ...*Computing**top*-*k**Closeness**Centrality**Faster**in**Unweighted**Graphs*ELISABETTA BERGAMINI, Karlsruhe Institute of Technology ...##
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Computing top-k Closeness Centrality Faster in Unweighted Graphs

2019
*
ACM Transactions on Knowledge Discovery from Data
*

The second one, Ocl, provides

doi:10.1145/3344719
fatcat:hfuu46acanb2bl7gqtxoklkuhi
*top**k**closeness**centralities*with high probability [23] . ... Conclusions*In*this paper we have presented a hardness result on the*computation*of the most*central*vertex*in*a*graph*, according to*closeness**centrality*. ...##
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Computing Top-k Closeness Centrality Faster in Unweighted Graphs

2015
*
2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX)
*

Thus, we present a new algorithm for

doi:10.1137/1.9781611974317.6
dblp:conf/alenex/BergaminiBCMM16
fatcat:jys7xwyckfas3m7nrflm6toe6i
*computing*this*top*-*k*ranking*in**unweighted**graphs*. Following the rationale of previous work, our algorithm significantly reduces the number of traversed edges. ... The currently best algorithms*in*practical applications for*computing*the*closeness*for all nodes exactly*in**unweighted**graphs*are based on breadth-first search (BFS) from every node. ... We would like to thank Moritz von Looz for providing the hyperbolic random*graphs*used*in*our experiments and numerous contributors to the NetworKit project. ...##
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Scaling up Group Closeness Maximization
[chapter]

2018
*
2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)
*

Finally, we study for the first time the correlation between the

doi:10.1137/1.9781611975055.18
dblp:conf/alenex/BergaminiGM18
fatcat:7njuuneb25dnnkqmbx2ilisxcm
*top*-*k*nodes with highest individual*closeness*and an approximation of the most*central*group*in*large complex networks. ... While the identification of the*k*nodes with highest*closeness*received significant attention, many applications are actually interested*in*finding a group of nodes that is*central*as a whole. ... The first element of |S| is*computed*using the parallel*top*-*k**closeness*implementation described*in*[4] . ...##
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New Approximation Algorithms for Forest Closeness Centrality – for Individual Vertices and Vertex Groups
[article]

2021
*
arXiv
*
pre-print

Moreover, our experiments show that on disconnected

arXiv:2101.06192v1
fatcat:v7ks2xvembdcxhr735xehmpbde
*graphs*, group forest*closeness*outperforms existing*centrality*measures*in*the context of semi-supervised vertex classification. ... A measure that is gaining attention is forest*closeness**centrality*; it is*closely*related to electrical measures using current flow but can also handle disconnected*graphs*. ... accuracy than strategies based on existing*centrality*measures (including*top*-*k*forest*closeness*). ...##
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K-path centrality

2011
*
Proceedings of the 4th Workshop on Social Network Systems - SNS '11
*

*Computing*

*K*-Path

*Centrality*2α ln n iterations:Real

*graphs*from online sources and our previous research.Synthetic social

*graphs*of 1K, 10K, 50K, and 100K nodes.Input :

*Graph*G = (V , E ), Array ... 1 − 1/n 2 -O(κ 3 n 2−2α log n) time on weighted/

*unweighted*

*graphs*, where α ∈ [−1/2, 1/2] controls the tradeoff between accuracy and

*computation*time 3 An empirical demonstration on real and synthetic ... 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 ...

##
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Scaling up Group Closeness Maximization
[article]

2019
*
arXiv
*
pre-print

While the identification of the

arXiv:1710.01144v2
fatcat:hcwr6pedo5gaziadfyfgqt3ssu
*k*nodes with highest*closeness*received significant attention, many applications are actually interested*in*finding a group of nodes that is*central*as a whole. ...*Closeness*is a widely-used*centrality*measure*in*social network analysis. For a node it indicates the reciprocal of the average shortest-path distance to the other nodes of the network. ...*closeness*and for other helpful discussions on the topic. ...##
###
Computing Top-k Closeness Centrality in Fully-dynamic Graphs
[chapter]

2018
*
2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)
*

Prior work has shown that

doi:10.1137/1.9781611975055.3
dblp:conf/alenex/BiseniusBAM18
fatcat:hf6wjs7uhzdebg6nrv7zdellvy
*computing*the*top*-*k*nodes with highest*closeness*can be done much*faster*than*computing**closeness*for all nodes*in*real-world networks. ... However, for many applications, it is only necessary to find the*k*most*central*nodes and not all*closeness*values. ...*Computing*the*closeness**centrality*of a node*in*an*unweighted**graph*requires a complete breadthfirst search (BFS) -or a complete run of Dijkstra's algorithm for weighted*graphs*. ...##
###
Estimating graph distance and centrality on shared nothing architectures

2014
*
Concurrency and Computation
*

*In*one experiment, we mined a real-world Web

*graph*with 700 million nodes and 12 billion edges to identify the most

*central*vertices and calculated more than 63 billion shortest paths

*in*6 h on a 20-node ... We present a parallel toolkit for pairwise distance

*computation*

*in*massive networks. ... For

*unweighted*

*graphs*, Brandes' algorithm [18] can

*compute*betweenness

*centrality*

*in*O.nm/ time. ...

##
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Laplacian centrality: A new centrality measure for weighted networks

2012
*
Information Sciences
*

We also prove an algebraic

doi:10.1016/j.ins.2011.12.027
fatcat:uwd35zzpvrfltlsfk6dg7u55y4
*graph*theory result that provides a structural description of the Laplacian*centrality*measure which is*in*terms of the number of all kinds of 2-walks. ... For*unweighted*networks where edges are just present or absent and have no weight attached, many*centrality*measures have been presented, such as degree, betweenness,*closeness*, eigenvector and subgraph ... The subgraph*centrality*of the vertex i is defined as P 1 k¼0 u*k*ði;iÞ*k*! , where u*k*(i, i) is the number of*closed**k*-walks that vertex i participates*in*the network. ...##
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Identifying high betweenness centrality nodes in large social networks

2012
*
Social Network Analysis and Mining
*

The randomized algorithm runs

doi:10.1007/s13278-012-0076-6
fatcat:kdnyzni375bf5o7hijfyoggdia
*in*time O(κ^3n^2-2α n) and outputs, for each vertex v, an estimate of its*k*-path*centrality*up to additive error of ± n^1/2+ α with probability 1-1/n^2. ... It introduces a new metric,*k*-path*centrality*, and a randomized algorithm for estimating it, and shows empirically that nodes with high*k*-path*centrality*have high node betweenness*centrality*. ... The authors would also like to acknowledge the use of the*computing*services provided by Research*Computing*, University of South Florida. ...##
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Efficient top-k closeness centrality search

2014
*
2014 IEEE 30th International Conference on Data Engineering
*

This paper presents a new technique that efficiently finds the

doi:10.1109/icde.2014.6816651
dblp:conf/icde/OlsenLH14
fatcat:bwxgcq6rxza5tjf35o7z4m26pa
*k*most*central*entities*in*terms of*closeness**centrality*. ... Since the cost of each*centrality**computation*may vary substantially depending on the choice of the previous*computation*, our technique schedules*centrality**computations**in*a manner that minimizes the ... Such a*top*-*k**centrality*problem can be formally defined as follows: Definition 2: (*Top*-*k**Closeness**Centrality*Problem) Given*graph*G(V, E), a*top*-*k**centrality*problem is to find: arg max V ′ ⊆V, V ′ ≥*k*...##
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Centrality Measures in Complex Networks: A Survey
[article]

2020
*
arXiv
*
pre-print

values

arXiv:2011.07190v1
fatcat:obm6lgm6ojaw5iuk3pigvomoo4
*in*dynamic networks, methods to identify*top*-*k*nodes, approximation algorithms, open research problems related to the domain, and so on. ... Some of these*centrality*measures can be*computed*using local information of the node, such as degree*centrality*and semi-local*centrality*measure. ... They further proposed a*faster*algorithm that involves parallel execution to*compute*the*k*-shell*in*larger*graphs*. ...##
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Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

2015
*
Neural Information Processing Systems
*

We show how the theta function can be interpreted as a measure of diversity

dblp:conf/nips/JohanssonCBD15
fatcat:hlomosgpxbdntdg525rhtlqcly
*in**graphs*and use this idea, and the*graph*embedding*in*algorithms for Max-Cut, correlation clustering and document summarization ... We show how it can be*computed*exactly by semidefinite programming, and how to approximate it using SVM*computations*. ... Acknowledgments This work is supported*in*part by the Swedish Foundation for Strategic Research (SSF). ...##
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Betweenness Centrality : Algorithms and Lower Bounds
[article]

2008
*
arXiv
*
pre-print

*In*this paper, we present a randomized parallel algorithm and an algebraic method for

*computing*betweenness

*centrality*of all nodes

*in*a network. ... Betweenness

*centrality*is the most widely used metric to measure the importance of a node

*in*a network. ... Acknowledgements This project is funded by ARC (Algorithms and Randomness Center) of the College of

*Computing*at Georgia Institute of Technology. ...

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