Filters








160,994 Hits in 3.6 sec

Betweenness Centrality -- Incremental and Faster [article]

Meghana Nasre, Matteo Pontecorvi, Vijaya Ramachandran
2013 arXiv   pre-print
We also give a static algorithm for computing betweenness centrality of all vertices that runs in time O(m* n + n^2 log n), which is faster than the Brandes algorithm on any graph with n log n = o(m) and  ...  The current widely used algorithm to compute the betweenness centrality of all vertices in G is the Brandes algorithm that runs in O(mn + n^2 log n) time, where n = |V| and m = |E|.  ...  Static betweenness centrality In this section we present static algorithms that compute betweenness centrality faster than the Brandes algorithm.  ... 
arXiv:1311.2147v3 fatcat:qzoizqpa4rf7xizwahuxfr6dhq

Betweenness Centrality – Incremental and Faster [chapter]

Meghana Nasre, Matteo Pontecorvi, Vijaya Ramachandran
2014 Lecture Notes in Computer Science  
We consider the incremental computation of the betweenness centrality (BC) of all vertices in a graph G = (V, E), directed or undirected, with positive real edge-weights.  ...  We also give a static BC algorithm that runs in time O(m * n + n 2 log n), which is faster than the Brandes algorithm on any graph with m = ω(n log n) and m * = o(m).  ...  Static betweenness centrality In this section we present static algorithms that compute betweenness centrality faster than the Brandes algorithm.  ... 
doi:10.1007/978-3-662-44465-8_49 fatcat:726or2gdznfzvkkhk3uidw5znq

Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs

F. A. Oliehoek, M. T. J. Spaan, C. Amato, S. Whiteson
2013 The Journal of Artificial Intelligence Research  
We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent.  ...  In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs.  ...  Acknowledgments We thank Raghav Aras and Abdeslam Boularias for making their code available to us.  ... 
doi:10.1613/jair.3804 fatcat:gephywcsprbbzmilyj6s575v2a

Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster

Marco A. Montes de Oca, Thomas Stuetzle, Mauro Birattari, Marco Dorigo
2010 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems  
Positive feedback and a consensus-building procedure are the key elements of a self-organized decision-making mechanism that allows a population of agents to collectively determine which of two actions  ...  The obtained experimental results show that by using the incremental social learning approach, the collective learning process can be accelerated substantially.  ...  The difference between these quantities served as a measure of the balance between exploration and exploitation.  ... 
doi:10.1109/saso.2010.28 dblp:conf/saso/OcaSBD10 fatcat:fwk4gnltgnabvigcfmab6uuv5a

Fully Dynamic Betweenness Centrality [chapter]

Matteo Pontecorvi, Vijaya Ramachandran
2015 Lecture Notes in Computer Science  
We present fully dynamic algorithms for maintaining betweenness centrality (BC) of vertices in a directed graph G = (V, E) with positive edge weights.  ...  We achieve an amortized O(ν * 2 · log 3 n) time per update with our basic algorithm, and O(ν * 2 · log 2 n) time with a more complex algorithm, where n = |V |, and ν * bounds the number of distinct edges  ...  Introduction Betweenness centrality (BC) is a widely-used measure in the analysis of large complex networks, and is defined as follows.  ... 
doi:10.1007/978-3-662-48971-0_29 fatcat:yh7hl4aapvgijgcajrvbejnqym

Faster Betweenness Centrality Updates in Evolving Networks [article]

Elisabetta Bergamini, Henning Meyerhenke, Mark Ortmann, Arie Slobbe
2017 arXiv   pre-print
For betweenness, several dynamic algorithms have been proposed over the years, targeting different update types (incremental- and decremental-only, fully-dynamic).  ...  Our method is a combination of two independent contributions: a faster algorithm for updating pairwise distances as well as number of shortest paths, and a faster algorithm for updating dependencies.  ...  BA, KDB, KWCC, and our new approach, which we refer to as iBet (from Incremental Betweenness).  ... 
arXiv:1704.08592v1 fatcat:kuz2fdd3vjby5h6tukiegurn7e

Approximating Betweenness Centrality in Large Evolving Networks [article]

Elisabetta Bergamini and Henning Meyerhenke and Christian L. Staudt
2014 arXiv   pre-print
Betweenness centrality ranks the importance of nodes by their participation in all shortest paths of the network. Therefore computing exact betweenness values is impractical in large networks.  ...  However, for dynamic networks, no approximation algorithm for betweenness centrality is known that improves on static recomputation.  ...  RK and our incremental approach.  ... 
arXiv:1409.6241v1 fatcat:upgmws5h5zf3tpi677ruik76ay

Approximating Betweenness Centrality in Large Evolving Networks [chapter]

Elisabetta Bergamini, Henning Meyerhenke, Christian L. Staudt
2014 2015 Proceedings of the Seventeenth Workshop on Algorithm Engineering and Experiments (ALENEX)  
Betweenness centrality ranks the importance of nodes by their participation in all shortest paths of the network. Therefore computing exact betweenness values is impractical in large networks.  ...  However, for dynamic networks, no approximation algorithm for betweenness centrality is known that improves on static recomputation.  ...  RK and our incremental approach.  ... 
doi:10.1137/1.9781611973754.12 dblp:conf/alenex/BergaminiMS15 fatcat:yahsuk4hbrgznhbociwwrt6jse

Fully-dynamic Approximation of Betweenness Centrality [article]

Elisabetta Bergamini, Henning Meyerhenke
2015 arXiv   pre-print
Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths.  ...  In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions.  ...  We also thank Matteo Riondato (Brown University) and anonymous reviewers for their constructive comments.  ... 
arXiv:1504.07091v2 fatcat:zhe7ockhavbn7kywum725wzf2y

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Fuad Jamour, Spiros Skiadopoulos, Panos Kalnis
2018 IEEE Transactions on Parallel and Distributed Systems  
Thus, the computation of betweenness centrality should be performed incrementally.  ...  We propose iCENTRAL; a novel incremental algorithm for computing betweenness centrality in evolving graphs.  ...  In this work we concentrate on the incremental computation of betweenness centrality.  ... 
doi:10.1109/tpds.2017.2763951 fatcat:jq24z6pxbrayjfzkydpsvs2uxm

Scalable Online Betweenness Centrality in Evolving Graphs [article]

Nicolas Kourtellis, Gianmarco De Francisci Morales, Francesco Bonchi
2015 arXiv   pre-print
The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date.  ...  In this paper we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges  ...  betweenness centrality of large evolving graphs, incrementally and online.  ... 
arXiv:1401.6981v2 fatcat:qsi5ain76fgpxjirpajgw4xzdm

A Faster Algorithm for Fully Dynamic Betweenness Centrality [article]

Matteo Pontecorvi, Vijaya Ramachandran
2015 arXiv   pre-print
We present a new fully dynamic algorithm for maintaining betweenness centrality (BC) of vertices in a directed graph G=(V,E) with positive edge weights.  ...  We achieve an amortized O((ν^*)^2 ^2 n) time per update, where n = |V| and ν^* bounds the number of distinct edges that lie on shortest paths through any single vertex.  ...  Introduction Betweenness centrality (BC) is a widely-used measure in the analysis of large complex networks, and is defined as follows.  ... 
arXiv:1506.05783v3 fatcat:is6mgwy245cu7nw6ptwqlo3gea

Computing betweenness centrality in external memory

Lars Arge, Michael T. Goodrich, Freek van Walderveen
2013 2013 IEEE International Conference on Big Data  
In this paper we describe the first known external-memory and cache-oblivious algorithms for computing betweenness centrality.  ...  Betweenness centrality is one of the most wellknown measures of the importance of nodes in a socialnetwork graph.  ...  Annotated Incremental BFS: To compute betweenness centrality, we first modify the incremental BFS algorithm above such that it also computes σ siv for each vertex v when computing the BFS tree from s i  ... 
doi:10.1109/bigdata.2013.6691597 dblp:conf/bigdataconf/ArgeGW13 fatcat:smhxlmehargiliaraljh5b5lq4

Improving the betweenness centrality of a node by adding links [article]

Elisabetta Bergamini, Pierluigi Crescenzi, Gianlorenzo D'Angelo, Henning Meyerhenke, Lorenzo Severini, Yllka Velaj
2018 arXiv   pre-print
Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network.  ...  In particular, we study the problem of maximizing the betweenness score of a given node -- Maximum Betweenness Improvement (MBI) -- and that of maximizing the ranking of a given node -- Maximum Ranking  ...  Since the augmented APSP update of iBet was shown to be significantly faster than all existing algorithms, we use it as a building block for our incremental algorithm for the betweenness centrality of  ... 
arXiv:1702.05284v2 fatcat:s5nzx3jicvg73fcz5dbgtrtzl4

Fully-Dynamic Approximation of Betweenness Centrality [chapter]

Elisabetta Bergamini, Henning Meyerhenke
2015 Lecture Notes in Computer Science  
Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths.  ...  In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions.  ...  We thank Moritz von Looz for providing the synthetic dynamic networks and the numerous contributors to the NetworKit project.  ... 
doi:10.1007/978-3-662-48350-3_14 fatcat:e332kxucpfefrg6jk3vsad7o6y
« Previous Showing results 1 — 15 out of 160,994 results