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Research map / Learning theory and algorithms / Submodular and game algorithms

Coverage Centrality Maximization in Undirected Networks

AAAI 2019 · Learning theory and algorithms › Submodular and game algorithms · approach: Submodular influence maximization · 22 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Greedy1 to select added edgescoverage centrality maximization · BA, CM, karate, windsurfers, jazz, haggleapproximation ratio to optimum: always greater than 0.97Helped
use Greedy2 to select added edgescoverage centrality maximization · BA, CM, karate, windsurfers, jazz, haggleapproximation ratio to optimum: always greater than 0.78Helped
use Greedy1 instead of Greedy2coverage centrality maximization · jazz and hagglecoverage centralityHelped
use Greedy1 or Greedy2 instead of random edge additioncoverage centrality maximization · jazz and hagglecoverage centralityHelped
increase the Greedy1 tuning parameter from t=2 to t=3coverage centrality maximization · jazz, k=10runtime: 7.5 seconds -> 242 secondsHurt
use Greedy2 instead of Greedy1 (t=2)coverage centrality maximization · jazz, k=10runtime: 7.5 seconds -> 4.1 secondsHelped
use Greedy2 as k increasescoverage centrality maximization · jazzscalabilityHelped

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