Published October 3, 2021 | Version v1
Journal article Open

Evaluating the role of community detection in improving influence maximization heuristics

  • 1. Innorenew CoE; Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska
  • 2. Innorenew CoE; Department of Applied Informatics, University of Szeged
  • 3. Department of Computer Science, Electrical and Space Engineering, Embedded Intelligent Systems Lab, Luleå University of Technology


Both community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.



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InnoRenew CoE – Renewable materials and healthy environments research and innovation centre of excellence 739574
European Commission