Preprocessing Rules for Target Set Selection in Complex Networks

  • Renato Silva Melo UFPR
  • André Luís Vignatti UFPR


In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

Palavras-chave: Influence Propagation, Social Networks, Combinatorial Optimization


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MELO, Renato Silva; VIGNATTI, André Luís. Preprocessing Rules for Target Set Selection in Complex Networks. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 9. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 108-119. ISSN 2595-6094. DOI: