<?xml version="1.0" encoding="UTF-8"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-07-22T01:28:55Z</responseDate>
  <request identifier="7609" metadataPrefix="oai_dc" verb="GetRecord">https://drops.dagstuhl.de/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:drops-oai.dagstuhl.de:7609</identifier>
        <datestamp>2024-03-06T10:39:50Z</datestamp>
        <setSpec>ddc:004</setSpec>
        <setSpec>open_access</setSpec>
      </header>
      <metadata>
        <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>Faster Betweenness Centrality Updates in Evolving Networks</dc:title>
          <dc:creator>Bergamini, Elisabetta</dc:creator>
          <dc:creator>Meyerhenke, Henning</dc:creator>
          <dc:creator>Ortmann, Mark</dc:creator>
          <dc:creator>Slobbe, Arie</dc:creator>
          <dc:subject>Graph algorithms</dc:subject>
          <dc:subject>shortest paths</dc:subject>
          <dc:subject>distances</dc:subject>
          <dc:subject>dynamic algorithms</dc:subject>
          <dc:description>Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many today’s networks, algorithms that quickly update centrality scores have become a necessity. For betweenness, several dynamic algorithms have been proposed over the years, targeting different update types (incremental- and decremental-only, fully-dynamic). In this paper we introduce a new dynamic algorithm for updating betweenness centrality after an edge insertion or an edge weight decrease. 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. Whereas the worst-case running time of our algorithm is the same as recomputation, our techniques considerably reduce the number of operations performed by existing dynamic betweenness algorithms. Our experimental evaluation on a variety of real-world networks reveals that our approach is significantly faster than the current state-of-the-art dynamic algorithms, approximately by one order of magnitude on average.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Elisabetta Bergamini and Henning Meyerhenke and Mark Ortmann and Arie Slobbe</dc:contributor>
          <dc:date>2017</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 75, 16th International Symposium on Experimental Algorithms (SEA 2017)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/LIPIcs.SEA.2017.23</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-76093</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2017.23</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
        </oai_dc:dc>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
